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As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users’ valuations of the service will affect a population’s behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data.

Blockchain offers the opportunity to use the transaction graph for financial governance, yet properties of this graph are understudied. One key question in this direction is the extent to which the transaction graph can serve as an early-warning indicator for large financial losses. In this article, we demonstrate the impact of extreme transaction graph activity on the intraday volatility of the Bitcoin prices series. Specifically, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility and characterize the types of chainlets that signify extreme losses. Using bars ranging from 15 minutes up to a day, we fit GARCH models with and without the extreme chainlets and show that the former exhibit superior Value-at-Risk backtesting performance.

Background Blockchain has emerged as a decentralized and distributed framework that enables tamper-resilience and, thus, practical immutability for stored data. This immutability property is important in scenarios where auditability is desired, such as in maintaining access logs for sensitive healthcare and biomedical data. However, the underlying data structure of blockchain, by default, does not provide capabilities to efficiently query the stored data. In this investigation, we show that it is possible to efficiently run complex audit queries over the access log data stored on blockchains by using additional key-value stores. This paper specifically reports on the approach we designed for the blockchain track of iDASH Privacy & Security Workshop 2018 competition. In this track, participants were asked to devise an efficient way to run conjunctive equality and range queries on a genomic dataset access log trail after storing it in a permissioned blockchain network consisting of 4 identical nodes, each representing a different site, created with the Multichain platform. Methods Multichain duplicates and indexes blockchain data locally at each node in a key-value store to support retrieval requests at a later point in time. To efficiently leverage the key-value storage mechanism, we applied various techniques and optimizations, such as bucketization, simple data duplication and batch loading by accounting for the required query types of the competition and the interface provided by Multichain. Particularly, we implemented our solution and compared its loading and query-response performance with SQLite, a commonly used relational database, using the data provided by the iDASH 2018 organizers. Results Depending on the query type and the data size, the run time difference between blockchain based query-response and SQLite based query-response ranged from 0.2 seconds to 6 seconds. A deeper inspection revealed that range queries were the bottleneck of our solution which, nevertheless, scales up linearly. Conclusions This investigation demonstrates that blockchain-based systems can provide reasonable query-response times to complex queries even if they only use simple key-value stores to manage their data. Consequently, we show that blockchains may be useful for maintaining data with auditability and immutability requirements across multiple sites.

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.

Background Genomic data is increasingly collected by a wide array of organizations. As such, there is a growing demand to make summary information about such collections available more widely. However, over the past decade, a series of investigations have shown that attacks, rooted in statistical inference methods, can be applied to discern the presence of a known individual’s DNA sequence in the pool of subjects. Recently, it was shown that the Beacon Project of the Global Alliance for Genomics and Health, a web service for querying about the presence (or absence) of a specific allele, was vulnerable. The Integrating Data for Analysis, Anonymization, and Sharing (iDASH) Center modeled a track in their third Privacy Protection Challenge on how to mitigate the Beacon vulnerability. We developed the winning solution for this track. Methods This paper describes our computational method to optimize the tradeoff between the utility and the privacy of the Beacon service. We generalize the genomic data sharing problem beyond that which was introduced in the iDASH Challenge to be more representative of real world scenarios to allow for a more comprehensive evaluation. We then conduct a sensitivity analysis of our method with respect to several state-of-the-art methods using a dataset of 400,000 positions in Chromosome 10 for 500 individuals from Phase 3 of the 1000 Genomes Project. All methods are evaluated for utility, privacy and efficiency. Results Our method achieves better performance than all state-of-the-art methods, irrespective of how key factors (e.g., the allele frequency in the population, the size of the pool and utility weights) change from the original parameters of the problem. We further illustrate that it is possible for our method to exhibit subpar performance under special cases of allele query sequences. However, we show our method can be extended to address this issue when the query sequence is fixed and known a priori to the data custodian, so that they may plan stage their responses accordingly. Conclusions This research shows that it is possible to thwart the attack on Beacon services, without substantially altering the utility of the system, using computational methods. The method we initially developed is limited by the design of the scenario and evaluation protocol for the iDASH Challenge; however, it can be improved by allowing the data custodian to act in a staged manner.

WOS: 000304396700032, PubMed ID: 22297653, Familial Mediterranean fever (FMF) is an auto-inflammatory disorder characterized by febrile attacks. Increased acute-phase reactants are characteristic during febrile attacks. Ghrelin is a natural G-protein that decreases secretion of pro-inflammatory cytokines and acts as anti-inflammatory agent. The aim of this study was to investigate whether there is any change in ghrelin levels and whether increases in ghrelin levels can be used as a marker in these patients. Thirty-seven male patients and 30 healthy men as a control group were included in the study. Blood samples were obtained for ghrelin measurements both before the attacks (pre-attack period; ghrelin 1 group) and during the attacks (ghrelin 2 group). Samples were kept at -80A degrees C until the analysis was conducted and plasma ghrelin levels were measured using an immune-sorbent assay method. Mean ghrelin levels measured during the attacks were significantly higher (11.01 +/- A 4.78 pg/ml) as compared to pre-attack levels (5.78 +/- A 2.17 pg/ml; p < 0.001). Similarly, mean ghrelin levels measured in FMF patients during an attack were significantly different from that of the control group (6.57 +/- A 4.13 pg/ml; p < 0.001). In this study, high ghrelin levels were measured during attacks in FMF patients. This finding is in line with previous results regarding the fact that inflammatory response arising during an FMF attack is an acute inflammatory event. Our findings suggest that ghrelin levels measured during FMF attacks could be used as a biochemical indicator for the FMF attack in FMF patients and that it could be used for support of the diagnosis of the disease.

In this paper, we study the problem of supporting multidimensional range queries on encrypted data. The problem is motivated by secure data outsourcing applications where a client may store his/her data on a remote server in encrypted form and want to execute queries using server's computational capabilities. The solution approach is to compute a secure indexing tag of the data by applying bucketization (a generic form of data partitioning) which prevents the server from learning exact values but still allows it to check if a record satisfies the query predicate. Queries are evaluated in an approximate manner where the returned set of records may contain some false positives. These records then need to be weeded out by the client which comprises the computational overhead of our scheme. We develop a bucketization procedure for answering multidimensional range queries on multidimensional data. For a given bucketization scheme, we derive cost and disclosure-risk metrics that estimate client's computational overhead and disclosure risk respectively. Given a multidimensional dataset, its bucketization is posed as an optimization problem where the goal is to minimize the risk of disclosure while keeping query cost (client's computational overhead) below a certain user-specified threshold value. We provide a tunable data bucketization algorithm that allows the data owner to control the trade-off between disclosure risk and cost. We also study the trade-off characteristics through an extensive set of experiments on real and synthetic data.

Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based services. Currently, there are two main approaches to protect the location privacy of users: (i) hiding locations inside cloaking regions (CRs) and (ii) encrypting location data using private information retrieval (PIR) protocols. Previous work focused on finding good trade-offs between privacy and performance of user protection techniques, but disregarded the important issue of protecting the POI dataset D. For instance, location cloaking requires large-sized CRs, leading to excessive disclosure of POIs (O(|D|) in the worst case). PIR, on the other hand, reduces this bound to $O(\sqrt{|D|})$ , but at the expense of high processing and communication overhead. We propose hybrid, two-step approaches for private location-based queries which provide protection for both the users and the database. In the first step, user locations are generalized to coarse-grained CRs which provide strong privacy. Next, a PIR protocol is applied with respect to the obtained query CR. To protect against excessive disclosure of POI locations, we devise two cryptographic protocols that privately evaluate whether a point is enclosed inside a rectangular region or a convex polygon. We also introduce algorithms to efficiently support PIR on dynamic POI sub-sets. We provide solutions for both approximate and exact NN queries. In the approximate case, our method discloses O(1) POI, orders of magnitude fewer than CR- or PIR-based techniques. For the exact case, we obtain optimal disclosure of a single POI, although with slightly higher computational overhead. Experimental results show that the hybrid approaches are scalable in practice, and outperform the pure-PIR approach in terms of computational and communication overhead.

Many data mining applications, such as spam filtering and intrusion detection, are faced with active adversaries. In all these applications, the future data sets and the training data set are no longer from the same population, due to the transformations employed by the adversaries. Hence a main assumption for the existing classification techniques no longer holds and initially successful classifiers degrade easily. This becomes a game between the adversary and the data miner: The adversary modifies its strategy to avoid being detected by the current classifier; the data miner then updates its classifier based on the new threats. In this paper, we investigate the possibility of an equilibrium in this seemingly never ending game, where neither party has an incentive to change. Modifying the classifier causes too many false positives with too little increase in true positives; changes by the adversary decrease the utility of the false negative items that are not detected. We develop a game theoretic framework where equilibrium behavior of adversarial classification applications can be analyzed, and provide solutions for finding an equilibrium point. A classifier's equilibrium performance indicates its eventual success or failure. The data miner could then select attributes based on their equilibrium performance, and construct an effective classifier. A case study on online lending data demonstrates how to apply the proposed game theoretic framework to a real application.

Privacy-preserving data mining--developing models without seeing the data --- is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data/databases nor the instances to be classified. Naive Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to that baseline, presenting protocols to develop a Naive Bayes classifier on both vertically as well as horizontally partitioned data.

Recent developments in information systems technologies have resulted in computerizing many applications in various business areas. As a result, data has become a critical asset in many organizations. Even more, amount of data that is generated is exploding. Many applications ranging from social networking to mobile phones create large amounts of potentially sensitive information related to individuals. In addition, recent advances in cloud computing enables the cheap storage and sharing of such large data sets. In many situations, such data could be linked and mined to gain useful insights for creating huge economic and societal value. At the same time, privacy and security concerns can prevent such usage. In addition, further safeguards are needed to build trust in the data, which is instrumental for making critical decisions. To address these challenges, and to capture the full value of large data, we need novel secure and privacy-aware data management solutions. This special issue presents high quality solution ideas related to the secure and privacy-aware data management challenges occurring in different application domains, such as online social networks, location based applications, data publishing, data outsourcing etc. The first paper of this issue: A Generic and Distributed Privacy Preserving Classification Method with A Worst-Case Privacy Guarantee by Madhushri Banerjee, Zhiyuan Chen and Aryya Gangopadhyay deals with building privacy preserving distributed classification models that are applicable for different classification tasks under worst-case privacy guarantees.

As the popularity and usage of social media exploded over the years, understanding how social network users’ interests evolve gained importance in diverse fields, ranging from sociological studies to marketing. In this paper, we use two snapshots from the Twitter network and analyze data interest patterns of users in time to understand individual and collective user behavior on social networks. Building topical profiles of users, we propose novel metrics to identify anomalous friendships, and validate our results with Amazon Mechanical Turk experiments. We show that although more than 80 % of all friendships on Twitter are created due to data interests, 83 % of all users have at least one friendship that can be explained neither by users’ past interest nor collective behavior of other similar users.

To the Editor, Primary neoplasm of the small bowel is very rare. This constitutes 7–8 % of all tumours of gastrointestinal tract and only 1 % of the malignant ones [1]. Adenocarcinoma is the most common histolopathologic subtype of carcinoma of the small bowel [2]. Adenocarcinoma risk increases with progressing age. Adults older than 65 years demonstrate an increased prevalence of small bowel carcinoma. Herein, we report of an unusual and rare case with jejunal adenocancer. A 66-year-old man was presented to the gastroenterology outpatient clinic for iron deficiency anemia symptoms such as weakness, fatigue, and palpitation. His physical examination did not show any pathologic signs. Laboratory analysis showed hemoglobin: 10 g/dL, ferritin: 0.9 (22–322) ng/dL, iron: 24 (53–167) mcg/dL, total iron binding capacity: 418 (155–300) lg/dL and normal electrolyte panel values. Abdominal ultrasound was normal. Performed esophagogastroduodenoscopy and colonoscopy revealed no pathology. Capsule endoscopy confirmed jejunal ulcer. Double balloon enteroscopy demonstrated this lesion which causes narrowness of the lumen of the jejunum and endoscopic biopsy was performed. Histopathological examination confirmed the presence of adenocarcinoma. Computerized tomography scan of the abdomen with intravenous and oral contrast showed no pathology. Surgical resection was performed. After 3 months, whole body positron emission tomography scan was normal. Small intestine adenocancer is a rare tumor and to our knowledge a barely published elderly cases in the literature. Because small intestine is relatively unattainable with routine endoscopy, most of the patients are diagnosed in advanced stage. A precise and early diagnosis is crucial. This is the most important prognostic factor for curative radical surgery [3]. Consequently, when a small bowel neoplasm is doubted, capsule endoscopy and double balloon enteroscopy are the most useful techniques [4].