93 results on '"Bogdanov, Dan"'
Search Results
2. UN Handbook on Privacy-Preserving Computation Techniques
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Archer, David W., Pigem, Borja de Balle, Bogdanov, Dan, Craddock, Mark, Gascon, Adria, Jansen, Ronald, Jug, Matjaž, Laine, Kim, McLellan, Robert, Ohrimenko, Olga, Raykova, Mariana, Trask, Andrew, and Wardley, Simon
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Computer Science - Computers and Society ,Computer Science - Cryptography and Security - Abstract
This paper describes privacy-preserving approaches for the statistical analysis. It describes motivations for privacy-preserving approaches for the statistical analysis of sensitive data, presents examples of use cases where such methods may apply and describes relevant technical capabilities to assure privacy preservation while still allowing analysis of sensitive data. Our focus is on methods that enable protecting privacy of data while it is being processed, not only while it is at rest on a system or in transit between systems. The information in this document is intended for use by statisticians and data scientists, data curators and architects, IT specialists, and security and information assurance specialists, so we explicitly avoid cryptographic technical details of the technologies we describe., Comment: 50 pages
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- 2023
3. Blueprints for Deploying Privacy Enhancing Technologies in E-Government
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Kamm, Liina, Bogdanov, Dan, Brito, Eduardo, Ostrak, Andre, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Bieker, Felix, editor, de Conca, Silvia, editor, Gruschka, Nils, editor, Jensen, Meiko, editor, and Schiering, Ina, editor
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- 2024
- Full Text
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4. ZK-SecreC: a Domain-Specific Language for Zero Knowledge Proofs
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Bogdanov, Dan, Jääger, Joosep, Laud, Peeter, Nestra, Härmel, Pettai, Martin, Randmets, Jaak, Sokk, Ville, Tali, Kert, and Valdma, Sandhra-Mirella
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Computer Science - Programming Languages ,Computer Science - Cryptography and Security - Abstract
We present ZK-SecreC, a domain-specific language for zero-knowledge proofs. We present the rationale for its design, its syntax and semantics, and demonstrate its usefulness on the basis of a number of non-trivial examples. The design features a type system, where each piece of data is assigned both a confidentiality and an integrity type, which are not orthogonal to each other. We perform an empiric evaluation of the statements produced by its compiler in terms of their size. We also show the integration of the compiler with the implementation of a zero-knowledge proof technique, and evaluate the running time of both Prover and Verifier., Comment: 75 pp
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- 2022
5. Towards a common performance and effectiveness terminology for digital proximity tracing applications
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Benzler, Justus, Bogdanov, Dan, Kirchner, Göran, Lueks, Wouter, Lucas, Raquel, Oliveira, Rui, Preneel, Bart, Salathe, Marcel, Troncoso, Carmela, and von Wyl, Viktor
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Computer Science - Computers and Society - Abstract
Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps - beyond what is required to fulfill its primary notification role - may facilitate an understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification matrix may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT that is not part of the original DPT design. Ultimately, the integration of further information for epidemic monitoring into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT notification and monitoring systems or intending to assess the added value of DPT relative to existing contact tracing systems.
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- 2020
6. Privacy-Preserving Analytics, Processing and Data Management
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Keerup, Kalmer, Bogdanov, Dan, Kubo, Baldur, Auran, Per Gunnar, Södergård, Caj, editor, Mildorf, Tomas, editor, Habyarimana, Ephrem, editor, Berre, Arne J., editor, Fernandes, Jose A., editor, and Zinke-Wehlmann, Christian, editor
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- 2021
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7. PE-BPMN: Privacy-Enhanced Business Process Model and Notation
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Pullonen, Pille, Matulevičius, Raimundas, Bogdanov, Dan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Carmona, Josep, editor, Engels, Gregor, editor, and Kumar, Akhil, editor
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- 2017
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8. Deploying Decentralized, Privacy-Preserving Proximity Tracing.
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TRONCOSO, CARMELA, BOGDANOV, DAN, BUGNION, EDOUARD, CHATEL, SYLVAIN, CREMERS, CAS, GÜRSES, SEDA, HUBAUX, JEAN-PIERRE, JACKSON, DENNIS, LARUS, JAMES R., LUEKS, WOUTER, OLIVEIRA, RUI, PAYER, MATHIAS, PRENEEL, BART, PYRGELIS, APOSTOLOS, SALATHÉ, MARCEL, STADLER, THERESA, and VEALE, MICHAEL
- Subjects
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CONTACT tracing , *COMMUNICABLE disease control , *APPLICATION software , *DATA privacy , *COMPUTER security , *COMPUTER operating systems - Abstract
This article explores developing a smartphone application, an app, to enable improved contact tracing to prevent the spread of infections. Topics include the key components of a digital contact tracing app- privacy-preserving systems, integration of the app into both the smartphone’s operating system and the public health system, and the challenges in app deployment.
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- 2022
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9. How the Estonian Tax and Customs Board Evaluated a Tax Fraud Detection System Based on Secure Multi-party Computation
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Bogdanov, Dan, Jõemets, Marko, Siim, Sander, Vaht, Meril, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Böhme, Rainer, editor, and Okamoto, Tatsuaki, editor
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- 2015
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10. A Secure Genetic Algorithm for the Subset Cover Problem and Its Application to Privacy Protection
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Bogdanov, Dan, Emura, Keita, Jagomägis, Roman, Kanaoka, Akira, Matsuo, Shin’ichiro, Willemson, Jan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Naccache, David, editor, and Sauveron, Damien, editor
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- 2014
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11. A Practical Analysis of Oblivious Sorting Algorithms for Secure Multi-party Computation
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Bogdanov, Dan, Laur, Sven, Talviste, Riivo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bernsmed, Karin, editor, and Fischer-Hübner, Simone, editor
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- 2014
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12. Privacy-Preserving Statistical Data Analysis on Federated Databases
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Bogdanov, Dan, Kamm, Liina, Laur, Sven, Pruulmann-Vengerfeldt, Pille, Talviste, Riivo, Willemson, Jan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Preneel, Bart, editor, and Ikonomou, Demosthenes, editor
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- 2014
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13. Deploying Secure Multi-Party Computation for Financial Data Analysis : (Short Paper)
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Bogdanov, Dan, Talviste, Riivo, Willemson, Jan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Keromytis, Angelos D., editor
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- 2012
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14. A Universal Toolkit for Cryptographically Secure Privacy-Preserving Data Mining
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Bogdanov, Dan, Jagomägis, Roman, Laur, Sven, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chau, Michael, editor, Wang, G. Alan, editor, Yue, Wei Thoo, editor, and Chen, Hsinchun, editor
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- 2012
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15. VirtualLife: Secure Identity Management in Peer-to-Peer Systems
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Bogdanov, Dan, Livenson, Ilja, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Daras, Petros, editor, and Ibarra, Oscar Mayora, editor
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- 2010
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16. Sharemind: A Framework for Fast Privacy-Preserving Computations
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Bogdanov, Dan, Laur, Sven, Willemson, Jan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Jajodia, Sushil, editor, and Lopez, Javier, editor
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- 2008
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17. How the Estonian Tax and Customs Board Evaluated a Tax Fraud Detection System Based on Secure Multi-party Computation
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Bogdanov, Dan, primary, Jõemets, Marko, additional, Siim, Sander, additional, and Vaht, Meril, additional
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- 2015
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18. Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications
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Lueks, Wouter, primary, Benzler, Justus, additional, Bogdanov, Dan, additional, Kirchner, Göran, additional, Lucas, Raquel, additional, Oliveira, Rui, additional, Preneel, Bart, additional, Salathé, Marcel, additional, Troncoso, Carmela, additional, and von Wyl, Viktor, additional
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- 2021
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19. High-performance secure multi-party computation for data mining applications
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Bogdanov, Dan, Niitsoo, Margus, Toft, Tomas, and Willemson, Jan
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- 2012
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20. A Secure Genetic Algorithm for the Subset Cover Problem and Its Application to Privacy Protection
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Bogdanov, Dan, primary, Emura, Keita, additional, Jagomägis, Roman, additional, Kanaoka, Akira, additional, Matsuo, Shin’ichiro, additional, and Willemson, Jan, additional
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- 2014
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21. A Practical Analysis of Oblivious Sorting Algorithms for Secure Multi-party Computation
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Bogdanov, Dan, primary, Laur, Sven, additional, and Talviste, Riivo, additional
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- 2014
- Full Text
- View/download PDF
22. Privacy-Preserving Statistical Data Analysis on Federated Databases
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Bogdanov, Dan, primary, Kamm, Liina, additional, Laur, Sven, additional, Pruulmann-Vengerfeldt, Pille, additional, Talviste, Riivo, additional, and Willemson, Jan, additional
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- 2014
- Full Text
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23. Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications
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Lueks, Wouter, Benzler, Justus, Bogdanov, Dan, Kirchner, Göran, Lucas, Raquel, Oliveira, Rui, Preneel, Bart, Salathé, Marcel, Troncoso, Carmela, von Wyl, Viktor; https://orcid.org/0000-0002-8754-9797, Lueks, Wouter, Benzler, Justus, Bogdanov, Dan, Kirchner, Göran, Lucas, Raquel, Oliveira, Rui, Preneel, Bart, Salathé, Marcel, Troncoso, Carmela, and von Wyl, Viktor; https://orcid.org/0000-0002-8754-9797
- Abstract
Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but also on seamless integration of health system processes such as laboratory testing, communication of results (and their validation), generation of notification codes, manual contact tracing, and management of app-notified users. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. For example, comparative effectiveness measures may require information generated outside the DPT system, e.g., from manual contact tracing. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps may facilitate understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification system may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT. Ultimately, the integration of further information (e.g., regarding exact exposure time) into DPT involves a trade-off between data granularity and linkage on the one hand, and
- Published
- 2021
24. A Universal Toolkit for Cryptographically Secure Privacy-Preserving Data Mining
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Bogdanov, Dan, primary, Jagomägis, Roman, additional, and Laur, Sven, additional
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- 2012
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25. Deploying Secure Multi-Party Computation for Financial Data Analysis
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Bogdanov, Dan, primary, Talviste, Riivo, additional, and Willemson, Jan, additional
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- 2012
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26. A new way to protect privacy in large-scale genome-wide association studies
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Kamm, Liina, Bogdanov, Dan, Laur, Sven, and Vilo, Jaak
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- 2013
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27. Sharemind: A Framework for Fast Privacy-Preserving Computations
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Bogdanov, Dan, primary, Laur, Sven, additional, and Willemson, Jan, additional
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- 2008
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28. From Keys to Databases:Real-World Applications of Secure Multi-Party Computation
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Archer, David W., Bogdanov, Dan, Lindell, Yehuda, Kamm, Liina, Nielsen, Kurt, Pagter, Jakob Illeborg, Smart, Nigel P., Wright, Rebecca N., Archer, David W., Bogdanov, Dan, Lindell, Yehuda, Kamm, Liina, Nielsen, Kurt, Pagter, Jakob Illeborg, Smart, Nigel P., and Wright, Rebecca N.
- Abstract
We discuss the widely increasing range of applications of a cryptographic technique called Multi-Party Computation. For many decades this was perceived to be of purely theoretical interest, but now it has started to find application in a number of use cases. We highlight in this paper a number of these, ranging from securing small high value items such as cryptographic keys, through to securing an entire database.
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- 2018
29. From Keys to Databases—Real-World Applications of Secure Multi-Party Computation
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Archer, David W, primary, Bogdanov, Dan, additional, Lindell, Yehuda, additional, Kamm, Liina, additional, Nielsen, Kurt, additional, Pagter, Jakob Illeborg, additional, Smart, Nigel P, additional, and Wright, Rebecca N, additional
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- 2018
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30. Rmind: A Tool for Cryptographically Secure Statistical Analysis
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Bogdanov, Dan, primary, Kamm, Liina, additional, Laur, Sven, additional, and Sokk, Ville, additional
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- 2018
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31. Implementation and Evaluation of an Algorithm for Cryptographically Private Principal Component Analysis on Genomic Data
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Bogdanov, Dan, primary, Kamm, Liina, additional, Laur, Swen, additional, and Sokk, Ville, additional
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- 2018
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32. Maturity and Performance of Programmable Secure Computation
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Archer, David W., primary, Bogdanov, Dan, additional, Pinkas, Benny, additional, and Pullonen, Pille, additional
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- 2016
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33. Students and Taxes: a Privacy-Preserving Study Using Secure Computation
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Bogdanov, Dan, primary, Kamm, Liina, additional, Kubo, Baldur, additional, Rebane, Reimo, additional, Sokk, Ville, additional, and Talviste, Riivo, additional
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- 2016
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34. Sharemind: programmeeritav turvaline arvutussüsteem praktiliste rakendustega
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Bogdanov, Dan
- Subjects
dissertation ,cryptography ,väitekiri ,dissertatsioonid ,krüptograafia ,confidential information ,multiparty computation ,Shareminder (software) ,ETD ,konfidentsiaalne info ,Shareminder (tarkvara) - Abstract
Väitekirja elektrooniline versioon ei sisalda publikatsioone., Kujutlege riigijuhti, kes soovib oma riigi ressursse mõistlikult kasutada ning hiljem teada, kas tema otsused on olnud õiged. Kõige selle jaoks peab ta koguma andmeid riigi ja selle alamate igapäevaelu kohta. Need andmed võivad sisaldada fakte inimeste eraelu (näiteks toimetuleku ja tervise) ning ettevõtete ärisaladuste kohta. Kaasaegses ühiskonnas ei tohi valitsus teada oma kodanike kohta liiga palju, sest teadmisest tulenev võim hakkab rikkuma inimeste isiklikku vabadust. Minu doktoritöö eesmärk on lubada tundlike andmete töötlemist ilma nende omaniku konfidentsiaalsust rikkumata. Selleks kasutame turvalist ühisarvutust. Turvaline ühisarvutus on krüptograafiline meetod, millega saab digitaalsel kujul informatsioon töödelda nii, et töötleja ei näe andmeid ega oska neid omanikega siduda. Turvalise ühisarvutuse tehnoloogiat saab kasutada andmete kogumiseks, analüüsiks ja koondtulemuste avaldamiseks privaatsust säilitaval moel. Töö tutvustab andmetöötlussüsteemi Sharemind, mis on mõeldud andmete turvaliseks töötlemiseks. Sharemind tugineb uudsetel turvalise ühisarvutuse võtetel, mis töötavad eriti hästi tänapäevaste digitaalsete arvutitega. Doktoritöö selgitab Sharemindi praktilisi turvagarantiisid ning mõõdetakse katseliselt selle jõudlust arvutitel. Sharemindi arvutusprotokolle saab vabalt ümber järjestada. Nii saame neid kasutada selleks, et arvutada statistilisi funktsioone või käivitada keerukamaid andmete töötlemise algoritme. Doktoritöö esitleb ka uut programmeerimiskeelt nimega SecreC, mis teeb Sharemindi rakendustes kasutamise oluliselt lihtsamaks. Sharemindi abil on loodud mitmeid katserakendusi, mis näitavad kuidas seda saab kasutada privaatsust säilitava statistilise analüüsi ja andmekaeve jaoks. Lisaks katsetustele on Sharemindi abil realiseeritud ka maailma esimene praktikas kasutatav turvalise ühisarvutuse rakendus, mis kasutab andmete vahetamiseks avaliku andmesidevõrku internet. Seda rakendust on Eesti Infotehnoloogia ja Telekommunikatsiooni Liit (ITL) kasutanud oma liikmete majandusandmete analüüsiks. Doktoritöös kirjeldatud meetodid on kasulikud nii valitsusele kui ettevõtetele, kes soovivad turvaliselt töödelda konfidentsiaalseid andmeid., Imagine the leader of a state who wants to make wise choices on how to use the nation’s budget and also wants to know, how these decisions pay off. For this, the leader needs data from the citizens and the companies. Often, this data is private to a person (like financial status and health) or a business secret to a company. In a modern society, there are limits on how much a government can learn about its subjects before the power given by knowing too much starts to erode the freedom of the people. The goal of this work is to allow sensitive data to be processed while preserving the confidentiality of the data owner. We achieve this by using secure multiparty computation. Secure multiparty computation is a cryptographic technique that allows digital information to be processed without letting the person who is doing the processing see the values or associate them with their source. We can use this technology to collect data, analyze it and publish the aggregated result without compromising the privacy of the people. The thesis introduces Sharemind – a framework for creating secure data processing applications. Sharemind is based on new secure multiparty computation protocol suite that can be efficiently executed on current computing technology. The thesis discusses the security guarantees that Sharemind provides and measures its performance on digital computers. The secure computation protocols of Sharemind can be freely reordered to calculate many statistical functions or to evaluate more complex algorithms on the data. The thesis presents SecreC – a programming language for simplifying the use of Sharemind in applications. Sharemind has been used for building several research prototypes that demonstrate privacy preserving statistics and data mining techniques. In addition, Sharemind has been used to implement the first real-world secure multiparty computation application that worked using the public internet. The application has been used for financial reporting by the Estonian Association of Information Technology and Telecommunications. The methods described in this thesis can help both the government and companies in securely processing confidential information.
- Published
- 2013
35. Kuidas teha turvaliselt arvutusi ühissalastatud andmetega
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Bogdanov, Dan
- Published
- 2007
36. Octree-based Space Models and Their Use in Solving Path Finding Problems
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Bogdanov, Dan, Isotamm, Ain, Tartu Ülikool. Matemaatika-informaatikateaduskond, and Tartu Ülikool. Arvutiteaduse instituut
- Subjects
bakalaureusetööd ,informatics ,infotehnoloogia ,infotechnology ,informaatika - Abstract
Käesolevas töös esitatakse meetodid teeotsingu läbiviimiseks kolmemõõtmelises ruumis. Esimeses peatükis defineeritakse lähteandmete vorming ning esitatakse kuupide kaheksandpuul põhinev analüüsi meetod ruumi mudeli loomiseks. Teine peatükk kirjeldab kahte erinevat võimalust otsingugraafi koostamiseks ning loetleb omadusi, mille abil erinevaid graafi koostamise meetodeid võrrelda. Kolmandas peatükis defineeritakse läbitavus-, kaalu- ja pöörangufunktsiooni abil mobiilse agendi profiil. Need kolm funktsiooni kirjeldavad agendi võimet ruumi läbida ning määravad selle optimaalsuse. Esitatakse reeglid otsingugraafi lihtsustamiseks konkreetse agendi profiili põhjal., This work explores additional methods for solving the unified path finding problem. Our previous work concentrated on finding the optimal path on a terrain presented as a heightmap. We extend the searchable area to three-dimensional space populated by convex geometrical objects.
- Published
- 2005
37. Domain-Polymorphic Programming of Privacy-Preserving Applications
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Bogdanov, Dan, primary, Laud, Peeter, additional, and Randmets, Jaak, additional
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- 2014
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38. From Input Private to Universally Composable Secure Multi-party Computation Primitives
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Bogdanov, Dan, primary, Laud, Peeter, additional, Laur, Sven, additional, and Pullonen, Pille, additional
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- 2014
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39. Actively secure two-party computation: Efficient Beaver triple generation
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Bogdanov, Dan, Perustieteiden korkeakoulu, School of Science, Tietotekniikan laitos, Aura, Tuomas|Laur, Sven, Pullonen, Pille, Bogdanov, Dan, Perustieteiden korkeakoulu, School of Science, Tietotekniikan laitos, Aura, Tuomas|Laur, Sven, and Pullonen, Pille
- Published
- 2013
40. Domain-polymorphic language for privacy-preserving applications
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Bogdanov, Dan, primary, Laud, Peeter, additional, and Randmets, Jaak, additional
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- 2013
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41. A feasibility analysis of secure multiparty computation deployments
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Bogdanov, Dan, Perustieteiden korkeakoulu, Tietotekniikan laitos, Aura, Tuomas, Laur, Sven, Rebane, Reimo, Bogdanov, Dan, Perustieteiden korkeakoulu, Tietotekniikan laitos, Aura, Tuomas, Laur, Sven, and Rebane, Reimo
- Abstract
Imagine a scenario where multiple companies hold valuable information and they want to combine their data for analysis that would benefit them all. In an honest world, the companies could do just that - combine their data. However, in the real world, they might not be able to share their data because of data privacy issues. A cryptographic solution to this problem would be to use secure multiparty computation (SMC). SMC is a useful tool for computing the result of an operation with the inputs of multiple parties, without revealing what the inputs were. As a result, we can perform computations on the data without disclosing it. General multiparty computation is communication heavy and therefore its performance is network bound. One goal of this work is to create a mathematical model for predicting the performance of SMC protocols depending on the network parameters. The model is based on a set of experiments performed on the SHAREMIND SMC framework in our specialized cluster system. We perform analysis of the constructed model and estimate the model parameters. To validate the model, we compare the predictions of the model with the actual algorithm execution time results on the cluster system. To see how the model performs on an alternative system, we deploy a SHAREMIND nodes in the cloud environment and perform the validation there. In the last part of the work, we assess the feasibility of SMC in the cloud environment. The analysis is based on a sample secure survey scenario.
- Published
- 2012
42. VirtualLife : Secure Identity Management in Peer-to-Peer Systems
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Bogdanov, Dan, Livenson, Ilja, Bogdanov, Dan, and Livenson, Ilja
- Abstract
The popularity of virtual worlds and their increasing economic impacthas created a situation where the value of trusted identification has risensubstantially. We propose an identity management solution that provides theuser with secure credentials and allows to decrease the required trust that theuser must have towards the server running the virtual world. Additionally, theidentity management system allows the virtual world to incorporate reputationinformation. This allows the “wisdom of the crowd” to provide more input tousers about the reliability of a certain identity. We describe how to use theseidentities to provide secure services in the virtual world. These include securecommunications, digital signatures and secure bindings to external services., QC 20120507
- Published
- 2009
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43. An Improved Type System for a Privacy-aware Programming Language and its Practical Applications
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Sokk, Ville, Bogdanov, Dan, and Randmets, Jaak
- Abstract
Privaatseid andmeid on tarvis analüüsida või töödelda mitmes valdkonnas, näiteks tehes poliitilisi otsusi kasutades riiklikke andmekogusid või pakkudes pilvepõhiseid teenuseid. Sharemind on raamistik turvalisust säilitavate rakenduste arendamiseks, mis võimaldab andmeid analüüsida ilma üksikuid väärtuseid avaldamata. Sharemind kasutab selleks turvalise ühisarvutuse tehnoloogiat. Sharemindi raamistikku kasutavad programmid on kirjutatud programmeerimiskeeles nimega SecreC. Sharemind ja SecreC toetavad erinevaid turvalise ühisarvutuse meetodeid, mida nimetame turvaaladeks. Erinevatel turvaaladel on erinevad turvagarantiid ja efektiivsus ning turvaala valik sõltub konkreetse rakenduse vajadustest, mistõttu peaks SecreC toetama erinevate turvaalade kasutamist vastavalt rakenduse nõuetele. Töö eesmärk on võimaldada SecreC keelele turvaalade lisamist lubades programmeerijal kirjeldada turvaala andmetüübid, aritmeetilised tehted ja tüübiteisendused SecreC keeles. Töö autor lõi keele täiendustele formaalselt kirjeldatud tüübisüsteemi, teostas muudatused SecreC kompilaatoris, kirjeldas muudatuste praktilisi rakendusi, tekkivaid uusi probleeme ja nende võimalikke lahendusi., Confidential data needs to be processed in many areas, for example when making policy decisions using goverment databases or when providing cloud-based services. Sharemind is a framework for developing privacy-preserving applications which allows data to be analysed without revealing individual values. Sharemind uses a technology called secure multi-party computation. Programs using the Sharemind framework are written in a programming language called SecreC. Sharemind and SecreC are designed to support multiple secure multi-party computation methods which we call protection domain kinds. Different protection domain kinds have different security guarantees and performance characteristics and the decision about which one to use depends on the problem at hand which means SecreC should support different protection domain kinds that solve the needs of different applications. The goal of this thesis is to make it easier to add protection domain kinds to the SecreC language by allowing the programmer to define the protection domain kind data types, arithmetic operations and type conversions in the SecreC language without changing the compiler. The author developed a formal type system for the proposed language extensions, implemented them in the SecreC language compiler, described practical applications, open problems and proposed solutions.
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- 2016
44. A Comprehensive Protocol Suite for Secure Two-Party Computation
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Siim, Sander, Bogdanov, Dan, and Pullonen, Pille
- Abstract
Turvaline ühisarvutus võimaldab üksteist mitte usaldavatel osapooltel teha arvutusi tundlikel andmetel nii, et kellegi privaatsed andmed ei leki teistele osapooltele. Sharemind on kaua arenduses olnud turvalise ühisarvutuse platvorm, mis jagab tundlikke andmeid ühissalastuse abil kolme serveri vahel. Sharemindi kolme osapoolega protokolle on kasutatud suuremahuliste rakenduste loomisel. Igapäevaelus leidub rakendusi, mille puhul kahe osapoolega juurustusmudel on kolme osapoolega variandist sobivam majanduslikel või organisatoorsetel põhjustel. Selles töös kirjeldame ja teostame täieliku protokollistiku kahe osapoolega turvaliste arvutuste jaoks. Loodud protokollistiku eesmärk on pakkuda kolme osapoolega juurutusmudelile võrdväärne alternatiiv, mis on ka jõudluses võrreldaval tasemel. Kahe osapoole vahelised turvalise aritmeetika protokollid tuginevad peamiselt Beaveri kolmikute ette arvutamisele. Selleks, et saavutada vajalikku jõudlust, oleme välja töötanud tõhusad ette arvutamise meetodid, mis kasutavad uudsel viisil N-sõnumi pimeedastuse pikendamise protokolle. Meie meetodite eeliseks on alternatiividest väiksem võrgusuhtluse maht. Töös käsitleme ka insenertehnilisi väljakutseid, mis selliste meetodite teostamisel ette tulid. Töös esitame kirjeldatud konstruktsioonide turvalisuse ja korrektsuse tõestused. Selleks kasutame vähem eelduseid, kui tüüpilised teaduskirjanduses leiduvad tõestused. Üheks peamiseks saavutuseks on juhusliku oraakli mudeli vätimine. Meie kirjeldatud ja teostatud täisarvuaritmeetika ja andmetüüpide vaheliste teisendusprotokollide jõudlustulemused on võrreldavad kolme osapoole protokollide jõudlusega. Meie töö tulemusena saab Sharemindi platvormil teostada kahe osapoolega turvalisi ühisarvutusi., Secure multi-party computation allows a number of distrusting parties to collaborate in extracting new knowledge from their joint private data, without any party learning the other participants' secrets in the process. The efficient and mature Sharemind secure computation platform has relied on a three-party suite of protocols based on secret sharing for supporting large real-world applications. However, in some scenarios, a two-party model is a better fit when no natural third party is involved in the application. In this work, we design and implement a full protocol suite for two-party computations on Sharemind, providing an alternative and viable solution in such cases. We aim foremost for efficiency that is on par with the existing three-party protocols. To this end, we introduce more efficient techniques for the precomputation of Beaver triples using oblivious transfer extension, as the two-party protocols for arithmetic fundamentally rely on efficient triple generation. We reduce communication costs compared to existing methods by using 1-out-of-N oblivious transfer extension in a novel way, and provide insights into engineering challenges for efficiently implementing these methods. Furthermore, we show security of our constructions using strictly weaker assumptions than have been previously required by avoiding the random oracle model. We describe and implement a large amount of integer operations and data conversion protocols that are competitive with the existing three-party protocols, providing an overall solid foundation for two-party computations on Sharemind.
- Published
- 2016
45. Turvalise ühisarvutuse rakendamine
- Author
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Talviste, Riivo, Laur, Sven, juhendaja, Bogdanov, Dan, juhendaja, and Tartu Ülikool. Matemaatika-informaatikateaduskond.
- Subjects
väitekirjad ,data protection ,secure multi-party computation ,dissertations ,cryptography ,juhtumiuuringud ,andmekaitse ,dissertatsioonid ,krüptograafia ,turvalised ühisarvutused ,ETD ,case analysis - Abstract
Andmetest on kasu vaid siis kui neid saab kasutada. Eriti suur lisandväärtus tekib siis, kui ühendada andmed erinevatest allikatest. Näiteks, liites kokku maksu- ja haridusandmed, saab riik läbi viia kõrghariduse erialade tasuvusanalüüse. Sama kehtib ka erasektoris - ühendades pankade maksekohustuste andmebaasid, saab efektiivsemalt tuvastada kõrge krediidiriskiga kliente. Selline andmekogude ühendamine on aga tihti konfidentsiaalsus- või privaatsusnõuete tõttu keelatud. Õigustatult, sest suuremahulised ühendatud andmekogud on atraktiivsed sihtmärgid nii häkkeritele kui ka ametnikele ja andmebaaside administraatoritele, kes oma õigusi kuritarvitada võivad. Seda sorti rünnete vastus aitab turvalise ühisarvutuse tehnoloogia kasutamine, mis võimaldab mitmed osapoolel andmeid ühiselt analüüsida, ilma et keegi neist pääseks ligi üksikutele kirjetele. Oma esimesest rakendamisest praktikas 2008. aastal on turvalise ühisarvutuse tehnoloogia praeguseks jõudnud seisu, kus seda juurutatakse hajusates rakendustes üle interneti ning seda pakutakse ka osana teistest teenustest. Käesolevas töös keskendume turvalise ühisarvutuse praktikas rakendamise tehnilistele küsimustele. Alustuseks tutvustame esimesi selle tehnoloogia rakendusi, tuvastame veel lahendamata probleeme ning pakume töö käigus välja lahendusi. Töö põhitulemus on samm-sammuline ülevaade sellise juurutuse elutsüklist, kasutades näitena esimest turvalise ühisarvutuse abil läbi viidud suuremahulisi registriandmeid hõlmavat uuringut. Sealhulgas anname ülevaate ka mittetehnilistest toimingutest nagu lepingute sõlmimine ja Andmekaitse Inspektsiooniga suhtlemine, mis tulenevad suurte organisatsioonide kaasamisest nagu seda on riigiasutused. Tulevikku vaadates pakume välja lahenduse, mis ühendab endas födereeritud andmevahetusplatvormi ja turvalise ühisarvutuse tehnoloogiat. Konkreetse lahendusena pakume Eesti riigi andmevahetuskihi X-tee täiustamist turvalise ühisarvutuse teenusega Sharemind. Selline arhitektuur võimaldaks mitmeid olemasolevaid andmekogusid uuringuteks liita efektiivselt ja turvaliselt, ilma üksikisikute privaatsust rikkumata., Data is useful only when used. This is especially true if one is able to combine several data sets. For example, combining income and educational data, it is possible for a government to get a return of investment overview of educational investments. The same is true in private sector. Combining data sets of financial obligations of their customers, banks could issue loans with lower credit risks. However, this kind of data sharing is often forbidden as citizens and customers have their privacy expectations. Moreover, such a combined database becomes an interesting target for both hackers as well as nosy officials and administrators taking advantage of their position. Secure multi-party computation is a technology that allows several parties to collaboratively analyse data without seeing any individual values. This technology is suitable for the above mentioned scenarios protecting user privacy from both insider and outsider attacks. With first practical applications using secure multi-party computation developed in 2000s, the technology is now mature enough to be used in distributed deployments and even offered as part of a service. In this work, we present solutions for technical difficulties in deploying secure multi-party computation in real-world applications. We will first give a brief overview of the current state of the art, bring out several shortcomings and address them. The main contribution of this work is an end-to-end process description of deploying secure multi-party computation for the first large-scale registry-based statistical study on linked databases. Involving large stakeholders like government institutions introduces also some non-technical requirements like signing contracts and negotiating with the Data Protection Agency. Looking into the future, we propose to deploy secure multi-party computation technology as a service on a federated data exchange infrastructure. This allows privacy-preserving analysis to be carried out faster and more conveniently, thus promoting a more informed government.
- Published
- 2016
- Full Text
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46. The Analysis and Design of a Privacy-Preserving Survey System
- Author
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Vaht, Meril and Bogdanov, Dan
- Abstract
Vajadus konfidentsiaalseid andmeid koguda ja analüüsida nõuab privaatsust säilitavate turvameetmete kasutusele võtmist. Käesolev magistritöö kirjeldab privaatsust säilitava, turvalisel ühisarvutusel põhineva küsitlussüsteemi prototüübi analüüsi ning disaini. Süsteemi äriprotsesside kirjeldamiseks on kasutatud tegevusskeeme, kasutuslugusid ning olekumasina skeemi. Lisaks kirjeldab töö süsteemi ülesehitust ning esitleb juurutusskeem. Prototüüp on realiseeritud töös kirjeldatud analüüsi põhjal ning süsteemi on lähitulevikus plaanis kasutada ka praktiliste küsitluste läbiviimiseks., There are many topics that are needed to be analyzed and, at the same time, the answers of respondents can not be public. Collecting sensitive data requires applying privacy-preserving security measures. This master's thesis describes the design and business processes of the prototype of a secure survey system using secure multi-party computation. The business processes of the system are introduced using activity diagrams, the use cases and the state machine diagram. The design of the system is also described in this paper and is illustrated with a deployment model. Based on the analysis, the prototype has been implemented and the system will be used to conduct real surveys in the near future.
- Published
- 2015
47. Secure Multi-party Computation Protocols from a High-Level Programming Language
- Author
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Siim, Sander, Bogdanov, Dan, and Laur, Sven
- Abstract
Turvalise ühisarvutuse abil on võimalik sooritada privaatsust säilitavaid arvutusi mitmelt osapoolelt kogutud andmetega. Tänapäeva digitaalses maailmas on andmete konfidentsiaalsuse tagamine üha raskemini teostatav. Turvalise ühisarvutuse meetodid nagu ühissalastus ja Yao sogastatud loogikaskeemid võimaldavad teostada privaatsust säilitavaid arvutusprotokolle, mis ei lekita konfidentsiaalseid sisendandmeid. Aditiivne ühissalastuse skeem on väga efektiivne algebraliste ringide tehete sooritamiseks fikseeritud bitilaiusega andmetüüpide peal. Samas on seda kasutades raske ehitada protokolle, mis nõuavad paindlikumaid bititaseme operatsioone. Yao sogastatud loogikaskeemide meetod töötab aga igasuguse bitilaiusega andmete peal ja võimaldab väärtustada mistahes Boole'i funktsioone. Neid kahte meetodit koos kasutades ehitame turvalise hübriidprotokolli, mis kujutab endast üldist meetodit privaatsust säilitavate arvutuste teostamiseks bitikaupa ühissalastatud andmete peal. Loogikaskeeme vajalikeks arvutusteks on lihtne saada kahe kaasaegse turvalise ühisarvutuse jaoks mõeldud kompilaatori abil, mis muundavad C programmi loogikaskeemiks --- PCF ja CBMC-GC. Meie hübriidprotokolli prototüüp privaatsust säilitaval arvutusplatvormil Sharemind saavutab praktilisi jõudlustulemusi, mis on võrreldavad teiste kaasaegsete lahendustega. Lisaks kahe osapoolega arvutustele pakub meie prototüüp võimekust teostada mitmekesiseid arvutusi üldises turvalise ühisarvutuse arvutusmudelis. Hübriidprotokoll ja loogikaskeemide kompilaatorid võimaldavad koos kasutades lihtsalt ja efektiivselt luua üldkasutatavaid turvalise ühisarvutuse protokolle mistahes Boole'i funktsioonide väärtustamiseks., Secure multi-party computation (SMC) enables privacy-preserving computations on data originating from a number of parties. In today's digital world, data privacy is increasingly more difficult to provide. With SMC methods like secret sharing and Yao's garbled circuits, it is possible to build privacy-preserving computational protocols that do not leak confidential inputs to other parties. The additive secret sharing scheme is very efficient for algebraic ring operations on fixed bit-length data types. However, it is difficult to build protocols that require robust bit-level manipulation. Yao's garbled circuits approach, in contrast, works on arbitrary bit-length data and allows the evaluation of any Boolean function. Combining the two methods, we build a secure hybrid protocol, which provides a general method for building arbitrary secure computations on bitwise secret-shared data. We are able to generate circuits for the protocol easily by using two state-of-the-art C to circuit compilers designed for SMC applications --- PCF and CBMC-GC. Our hybrid protocol prototype on the Sharemind privacy-preserving computational platform achieves practical performance comparable to other recent work. In addition to two-party computations, our prototype provides the ability to perform a set of diverse computations in a generic SMC computational model. The hybrid protocol together with the circuit compilers provides a simple and efficient toolchain to build general-purpose SMC protocols for evaluating any Boolean function.
- Published
- 2014
48. A Secure Multi-Party Computation Protocol Suite Inspired by Shamir's Secret Sharing Scheme
- Author
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Turban, Tiina, Mjølsnes, Stig Frode, Bogdanov, Dan, Laur, Sven, and Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for telematikk
- Abstract
Secure multi-party computation allows us to perform analysis on private data without compromising it. Therefore, practical solutions for SMC are very welcome and Sharemind is one of the examples of such frameworks. There are already various protocol suites implemented on Sharemind, such as an additive three-party protocol suite. In this thesis, we designed and implemented a protocol suite, that was inspired by Shamir's secret sharing scheme. The latter is a popular way to divide a secret into pieces, called shares. The main result of this thesis are the implemented protocols with correctness and security proofs. We created a new protection domain kind \pdname{shamirnpp}, that allows one to create protection domains for various $n$-out-of-$k$ Sharmir's secret-sharing schemes. This PDK can now be used to write secure applications in the SecreC language. More specifically, we implemented protocols for addition, multiplication, boolean arithmetic and comparison operations. These protocols are the building blocks for various other functions one would want to possess, when analysing private data. As Sharemind has a standard library and a possibility to write domain-polymorphic code, many additional features, such as the absolute value function, can already be used with our newly implemented PDK. The goal of this work was to explore another SMC implementation option and compare it to the existing one on Sharemind. Our new protection domain kind based on Shamir's scheme was compared to \pdname{additive3pp}. Looking at simpler protocols, such as declassification or multiplication, we saw that our SMC algorithms offer better theoretical complexity. That was also evident from the benchmarking results for smaller input sizes. For larger inputs and more complicated operations, such as equality testing and less-than comparison, we had to admit \pdname{additive3pp} being better. One of the reasons, for the performance difference, is our naive implementations for \cmd{Conjunct} and \cmd{PrefixAND} algorithms. Many other algorithms depend on their performance, see Figure~\ref{fig:relations}, and improving it would improve the speed of equality testing and less-than comparison.This brings us to future work. As mentioned before, some of the protocols from this thesis could be improved. There are also other algorithms that could be added to our protocol suite. For example, it may be useful, if we could convert shares into a different PD's shares. In this thesis, we in theory separated the offline and online phase, in practice, we did not. Shamir's $k$-out-of-$n$ threshold scheme would allow to handle some \CPs disappearing or dealing with more corrupted parties. Exploring the implementation specifics of protocol interruption is an interesting topic for further research.
- Published
- 2014
49. Actively Secure Two-Party Computation: Efficient Beaver Triple Generation
- Author
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Pullonen, Pille, Laur, Sven, Aura, Tuomas, Bogdanov, Dan, Tartu Ülikool. Matemaatika-informaatikateaduskond, and Tartu Ülikool. Arvutiteaduse instituut
- Subjects
informatics ,magistritööd ,infotehnoloogia ,infotechnology ,informaatika - Abstract
Töö kombineerib erinevaid ideid, et saavutada aktiivses mudelis turvalist kahe osapoolega ühisarvutust. Töö käigus defineerime Sharemindi raamistikku kaks uut turvaala. Kasutame aditiivset ühissalastust, sõnumiautentimisskeeme, aditiivselt homomorfset krüptosüsteemi ning nullteadmustõestusi. Protokollistikud jagame kahte osasse, vastavalt ettearvutamise ja töö faas. Ettearvutamise ajal valmistatakse ette juhuslikke väärtusi, mis võimaldavad töö faasis arvutusi kiirendada. Eelkõige keskendume korrutamise jaoks vajalike Beaveri kolmikute genereerimisele., This thesis combines currently popular ideas in actively secure multi-party computation to define two actively secure two-party protocol sets for Sharemind secure multi-party computation framework. This includes additive secret sharing, dividing work as online and precomputation phase, using Beaver triples for multiplication and using message authentication codes for integrity checks. Our protocols use additively homomorphic Paillier cryptosystem, especially in the precomputation phase. The thesis includes two different setups for secure two-party computation which are also implemented and compared to each other. In addition, we propose new ideas to use additively homomorphic cryptosystem to generate Beaver triples for any chosen modulus. The important aspects of Beaver triple generation are maximising the amount of useful bits we get from one generation and assuring that these triples are correct.
- Published
- 2013
50. A Feasibility Analysis of Secure Multiparty Computation Deployments
- Author
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Rebane, Reimo, Bogdanov, Dan, Laur, Sven, Aura, Tuomas, Tartu Ülikool. Matemaatika-informaatikateaduskond, and Tartu Ülikool. Arvutiteaduse instituut
- Subjects
informatics ,magistritööd ,infotehnoloogia ,infotechnology ,informaatika - Abstract
Vaatleme stsenaariumi, kus mitu organisatsiooni sooviks oma individuaalsetest andmebaasidest ehitada ühe suure andmebaasi. Andmebaasi ehitamise eesmärgiks on ühiselt teostada arvutusi, mis oleksid kasulikud kõikidele osapooltele. Ühest küljest võivad kõik osapooled oma andmed avalikustada ning selle põhjal vajalikke arvutusi teha. Teisest küljest, ei ole kõiki andmeid võimalik avalikustada ning suur osa kasulikke arvutusi tehakse tõenäoliselt just privaatsete andmete pealt. Andmete avalikustamist võivad takistada nii organisatsiooni sisesed reeglid, kui ka seadused. Antud probleemile on olemas krüptograafiline lahendus - turvaline ühisarvutus. Turvalise ühisarvutuse abil saavad osapooled teha arvutusi nii, et iga osapool saab teada ainult arvutuse tulemuse ja ei saa teada midagi uut lähteandmete kohta. Käesolevas töös uurime ühe konkreetse turvalise ühisarvutuste raamistiku, Sharemindi, rakenduste jõudlust. Praegune Sharemindi rakendusserver töötab kolme masina peal, mis omavahel suheldes teostavad arvutusi. Antud raamistikus kasutatava turvalise ühisarvutuse jõudlus sõltub peamiselt edastatud andmete mahust ning seega võrgu jõudlusest, mille peal arvutusi läbi viiakse. Me ehitasime lineaarse regressioonimudeli, mille eesmärgiks on ennustada protokollide tööaega sõltuvalt võrgu parameetritest. Baasmudeli loomisel fikseerisime võrgu parameetrid olemasolevate tööriistadega ning hindasime mudeli parameetrite väärtused. Eksperimendid mudeli loomiseks viisime läbi eriotstarbelisel Sharemindi arvutusklastril. Teades mudeli parameetrite väärtuseid üritasime võrgu parameetrite põhjal ennustada mudeli tööaega. Klasti süsteemi peal valideerisime mudelit, ennustades algoritmide tööaega. Uurisime Apriori andmekaeve algoritmi, mis kasub Sharemindi turvalise ühisarvutuse protokolle. Ennustuse tulemused olid lähedased tegelikule protokollidele kulutatud ajale. Mudeli valideerimiseks paigaldasime Sharemindi raamistiku mitmelt pilveteenuse pakkujalt renditud taristule. Pilveteenused kiirendavad mitmesuguste rakenduste, eelkõige veebiteenuste arendusprotsessi, minimaliseerides esmast investeeringut, sest alustavad firmad ei pea oma riistvara hankima. Riistvara soetamise ja haldamise kulud vahetatakse pilveteenuse vastu. Pilve keskkonnas ei õnnestunud meil täpseid ennustusi protokollide tööaja kohta teha. Küll aga õnnestus teha umbkaudseid hinnanguid mudeli parameetrite kohta ning nende põhjal prtokollide tööaega hinnata. Kuigi hinnangud ei olnud väga täpsed, saime järeldada, et meie mudel ei ole vale, aga me ei suuda mudeli sisendparameetreid, võrgu latentsust ja ribalaiust, täpselt mõõta ning seetõttu on ka meie ennustused ebatäpsed. Selles töös uurisime ka turvalisel ühissalastusel põhinevate pilverakenduste majanduslikku otstarbekust. Jälgisime kahte aspekti: kas turvaline ühisarvutus pilves on piisavalt kiire ning kas kulud on mõistlikud. Leidsime, et arvutuste jõudlus on piisav mitmete potentsiaalsete rakenduste jaoks. Turvalise küsimustiku näitestsenaariumi põhjal järeldasime, et turvalise ühisarvutuse kulud pilverakendustes on samuti mõistlikud. Pilvekeskonnas on kaks peamist kuluallikat: serveri ülalhoidmiskulud ning võrguliiluse kulud. Leidsime, et suure hulga rakenduste jaoks on serveri ülalhoidmine kulukam kui andmeedastus. Selle töö tulemusena leidsime, et turvaline ühissalastus on mõistlik lahendus selliste rakenduste puhul, kus andmete privaatsuse tagamine on kriitilise tähtsusega. Tulemused näitavad, et Sharemindil põhinevad rakendused on praktilised ka siis, kui nad on juurutatud üle maailma laiali asuvates serverites. Lisaks näitasid meie katsed, et Sharemindi protkollide jõudlust saaks tõsta parandades vaba oleva võrgu ribalaiuse kasutamist raamistiku protokollide poolt., Imagine a scenario where multiple companies hold valuable information and they want to combine their data for analysis that would benefit them all. In an honest world, the companies could do just that - combine their data. However, in the real world, none of them can afford to make their data public because it could compromise their competitive advantage. One can easily find many similar real-world scenarios where there are privacy issues concerned with the data. Data privacy is also a very prominent issue when outsourcing the computing resources, for example, to cloud services. A cryptographic solution to this problem would be to use secure multiparty computation (SMC). SMC is a useful tool for computing the result of an operation with the inputs of multiple parties, without revealing what the inputs were. As a result, we can perform computations on the data without disclosing it. There exist two main approaches how to perform SMC. First, circuit evaluation, which is based on computations on arithmetic or logic circuits and is CPU intensive (CPU-bound). Second, general multiparty computation, which relies more on the communication between the parties (network-bound). Currently, the more efficient systems in this field use the latter approach. The theoretical complexity of these systems is well known. However, for real-life deployments the theoretical results alone are not enough. In this work, we would like to study the practical performance of the network-bound general multiparty computation. Based on the published results, the Sharemind SMC framework has shown the best performance and widest functionality among similar systems. One goal of this work was to create a mathematical model for predicting computational performance of SMC depending on the network parameters. The model was constructed based on a set of experiments conducted on th Sharemind framework. To validate our model, we set up a set of servers in the cloud environment. In this setting we measured the parameters of the network connections between the machines. The predictions were compared to the actual computation results. We were unable to accurately predict the running time of the protocols in the cloud. However, we concluded that this result was probably due to the inability to accurately estimate the effective network parameters to compute the model coefficients. The model validation in the experiment cluster environment showed that the models can be used to accurately predict the running time of the secure operations inside more complex algorithms. In the last part of the work, we utilized the general model to assess the feasibility of SMC in the cloud environment. With the model, we computed time estimations for executing certain operations in a sample scenario. The cost estimation showed that for a secure survey scenario, the cost of the secure computations is low compared to the cost of keeping the server running during the data gathering phase for the survey. The cost of the performed operations only starts to play a role with large data sets and computation heavy algorithms. The results of this work indicate that it is indeed feasible to do secure multiparty computation in the cloud environment for a whole range of real-world scenarios. This mainly benefits the potential cloud service scenarios where privacy of the stored data is one of the primary concerns. In this work we also found some indications of possible improvements to the Sharemind framework. We noticed that even though the protocols have a lot of avai\-lable bandwidth, they are not using it. For high throughput connections, the performance of the protocols may be significantly increased if the bandwidth utilization rate can be improved. As a future work, we could try other approaches to construct the models or, alternatively, build a specialized tool to measure the bandwidth parameter for the models.
- Published
- 2012
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