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Objective Supporting public health research and the public’s situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data. Materials and Methods The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework’s effectiveness in maintaining the PK11 threshold of 0.01. Results When sharing COVID-19 county-level case data across all US counties, the framework’s approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%. Conclusion Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.

In recent years, as online social networks continue to grow in size, estimating node features, such as sociodemographics, preferences and health status, in a scalable and reliable way has become a primary research direction in social network mining. Although many techniques have been developed for estimating various node features, quantifying uncertainty in such estimations has received little attention. Furthermore, most existing methods study networks parametrically, which limits insights about necessary quantity of queried data, reliable feature estimation, and estimator uncertainty. Uncertainty quantification is critical for answering key questions, such as, given a limited availability of social network data, how much data should be queried from the network?, and which node features can be learned reliably? More importantly, how can we evaluate uncertainty of our estimators? Uncertainty quantification is not equivalent to network sampling but constitutes a key complementary concept to sampling and the associated reliability analysis. To our knowledge, this paper is the first work that sheds light on uncertainty quantification and uncertainty propagation in social network feature mining. We propose a novel non-parametric bootstrap method for uncertainty analysis of node features in social network mining, derive its asymptotic properties, and demonstrate its effectiveness with extensive experiments. Furthermore, we develop a new metric based on dispersion of estimations, enabling analysts to assess how much more information is needed for increasing prediction reliability based on the estimated uncertainty. We demonstrate the effectiveness of our new uncertainty quantification methodology with extensive experiments on real life social networks, and a case study of mental health on Twitter.

A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, 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., Comment: JEL Classification: C58, C63, G18

With a spatial statistical database covering a large region, how to publish differential privacy protected information is a challenge. In previous works, information was published using large fixed spatial cells. In this paper, we develop novel flexible methods to publish the spatial information, which allows the users to freely move around the large region, zoom in and zoom out at arbitrary locations, and obtain information over spatial areas both large and small. We develop two methods to publish the spatial information protected under differential privacy. First the region is divided into the smallest spatial cells, where each cell does not observe an event happening more than once. Given repeated measurements, such as multiple day data, the noise added Bernoulli probabilities are computed for all the smallest spatial cells. For larger spatial cells of high interests to users, the noisy Bernoulli probabilities are combined into noisy Poisson-Binomial distributions which also satisfy differential privacy requirement. We use the New York Taxi data in the experiments to demonstrate how our methods work. We show that both of our methods are accurate, while the noisy count probabilities directly obtained from fixed large spatial cells often generate the spatial counts much smaller than the true values.

Objective Non-Hodgkin's lymphomas arising from tissues other than primary lymphatic sites are classified as primary extranodal lymphomas (PEL). PELs of the gastrointestinal system (PGISL) originate from the lymphatic tissues within the gastrointestinal tract. The prognostic value of 18F-FDG PET/CT in lymphomas is high in terms of both overall survival (OS) and disease-free survival (DFS). Our aim was to investigate the uptake patterns and properties of low-grade and high-grade PGISL on primary staging 18F-FDG PET/CT, as well as the prognostic significance of metabolic tumor parameters in high grade PGISL. Methods Thirty-nine patients with PGISL were enrolled in this retrospective cohort study between 2004-2015. Primary staging 18F-FDG PET/CT have been performed and quantitative parameters of SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG) have been calculated for all patients prior to treatment. Low-grade and high-grade PGISL were compared in terms of metabolic tumor parameters. Cox regression models were performed to determine factors that correlate with DFS in high-grade PGISL. Results There were statistically significant differences between high-grade and low-grade PGISL in terms of SUVmax, SUVmean, MTV, TLG, recurrence, mortality, DFS and OS. None of the potential risk factors (sex, age, site, SUVmax, SUVmean, MTV, TLG) for recurrence and metastasis in high grade PGISL was identified as a risk factor on univariate and multivariate Cox regression analysis. Conclusion Metabolic tumor parameters are not predictive markers in high-grade PGISL, especially in diffuse large B cell variant and primary gastric lymphoma. The first implications suggest they will not play a role in patient management.

Inference problem has always been an important and challenging topic of data privacy in databases. In relational databases, the traditional solution to this problem was to define views on relational schemas to restrict the subset of attributes and operations available to the users in order to prevent unwanted inferences. This method is a form of decomposition strategy, which mainly concentrates on the granularity of the accessible fields to the users, to prevent sensitive information inference. Nowadays, due to increasing data sharing among parties, the possibility of constructing complex indirect methods to obtain sensitive data has also increased. Therefore, we need to not only consider security threats due to direct access to sensitive data but also address indirect inference channels using functional and probabilistic dependencies (e.g., deducing gender of an individual from his/her name) while creating security views. In this paper, we propose a proactive and decomposition based inference control strategy for relational databases to prevent direct or indirect inference of private data. We introduce a new kind of context dependent attribute policy rule, which is named as security dependent set, as a set of attributes whose association should not be inferred. Then, we define a logical schema decomposition algorithm that prevents inference among attributes in security dependent set. The decomposition algorithm takes both functional and probabilistic dependencies into consideration in order to prevent all kinds of known inferences of relations among the attributes of security dependent sets. We prove that our proposed decomposition algorithm generates a secure logical schema that complies with the given security dependent set constraints. Since our proposed technique is purely proactive, it does not require any prior knowledge about executed queries and do not need to modify any submitted queries. It can also be embedded into any relational database management system without changing anything in the underlying system. We empirically compare our proposed method with the state of art reactive methods. Our extensive experimental analysis, conducted using TPC-H 1 benchmark scheme, shows the effectives our proposed approach.

At the 2015 Workshop on Security and Privacy Analytics, there was a well-attended and vigorous debate on educating and training professionals and students in security analytics. This article extends this debate by laying out essential security analytics concepts for professionals and students, sharing educational experiences, and identifying gaps in the field.

Celiac disease (CD) is an immune-mediated and chronic inflammatory enteropathy, triggered by the ingestion of gluten in genetically susceptible individuals. Immunoglobulin G4 (IgG4)-related diseases are a recently defined emerging clinical condition, characterized by increased serum IgG4 concentrations. The aim of this study was to investigate whether IgG4 levels correlate with the titers of intestinal antibodies and the degree of mucosal damage in CD patients.A total of 41 CD patients and 28 healthy controls were included in the study. All patients underwent a duodenal biopsy and were then diagnosed with the modified Marsh classification. Blood samples were obtained for IgG4 measurements. Serums were kept at -80 °C until the analysis was carried out and plasma IgG4 levels were determined using an enzyme-linked immune sorbent assay method with a diagnostic cutoff value of 135 mg/dl.The mean age of the CD and the control group was 26.8 ± 8.3 and 26.9 ± 6.2 years, respectively. The mean IgG4 levels were significantly higher in CD patients (283.21 ± 39.02 mg/dl) compared with the healthy control group (68.97 ± 15.89 mg/dl, P0.0001). In the CD group, 27/41 patients and in the control group, 4/28 patients had high IgG4 levels (135 mg/dl, P0.0001). A close correlation was found between the grade of mucosal damage, IgG4 levels, and antigliadin-IgA; the higher the grade Marsh score, the higher the measured IgG4 (P0.05).In our study, IgG4 levels of CD patients were higher than normal ranges whereas the results of the control group were within physiologic limits. We also showed for the first time that there was a correlation between IgG4 levels, autoantibody, and severity of mucosal damage. To the best of our knowledge, this is the first study to evaluate IgG4 levels and mucosal damage in CD patients.

Background: The effect of the timing of the second laparotomy on wound healing is not clear. In an experimental study in rats, we aimed to investigate the effect of timing on wound healing after reoperations on the same surgical site. Material and methods: Forty-eight rats were divided into four groups. The control group (GC) didn't have another laparotomy whereas the relaparotomies on the same surgical site were performed either on the 3rd, 15th or the 30th postoperative days in the three study groups (G3, G15, G30 respectively). The midline tension pressure, collagen types I, III and, histological analysis were performed from the specimens in order to assess the wound healing and strength. Results: The tensile strength was the highest in GC and decreased gradually in G3, G15 and G30, the difference between the groups did not reach statistical significance. Higher collagen levels, increased fibrosis, and large defects were observed in relaparotomy groups than CG. The musculoaponeurotic gap was shortest in GC when compared to other three relaparotomy groups (P

The process of record linkage seeks to integrate instances that correspond to the same entity. Record linkage has traditionally been performed through the comparison of identifying field values ( e.g. , Surname ), however, when databases are maintained by disparate organizations, the disclosure of such information can breach the privacy of the corresponding individuals. Various private record linkage (PRL) methods have been developed to obscure such identifiers, but they vary widely in their ability to balance competing goals of accuracy, efficiency and security. The tokenization and hashing of field values into Bloom filters (BF) enables greater linkage accuracy and efficiency than other PRL methods, but the encodings may be compromised through frequency-based cryptanalysis. Our objective is to adapt a BF encoding technique to mitigate such attacks with minimal sacrifices in accuracy and efficiency. To accomplish these goals, we introduce a statistically-informed method to generate BF encodings that integrate bits from multiple fields, the frequencies of which are provably associated with a minimum number of fields. Our method enables a user-specified tradeoff between security and accuracy. We compare our encoding method with other techniques using a public dataset of voter registration records and demonstrate that the increases in security come with only minor losses to accuracy.

Ensuring appropriate user access to information is a key objective of information security on a computer network. We propose a solution in which an individual user is assigned a universal trust score determined by applying the same model financial institutions use to determine an individual's credit risk score. This universal trust score is combined with the Bell-La Padula model to control the individual's access to information across multiple assured information sharing systems.

In many cases, competing parties who have private data may collaboratively conduct privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. Most often, the competing parties have different incentives. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether participating parties are truthful about their private input data. Unless proper incentives are set, current PPDA techniques cannot prevent participating parties from modifying their private inputs.incentive compatible privacy-preserving data analysis techniques This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful inputs. In this paper, we first develop key theorems, then base on these theorems, we analyze certain important privacy-preserving data analysis tasks that could be conducted in a way that telling the truth is the best choice for any participating party.

Record linkage is the task of identifying records from disparate data sources that refer to the same entity. It is an integral component of data processing in distributed settings, where the integration of information from multiple sources can prevent duplication and enrich overall data quality, thus enabling more detailed and correct analysis. Privacy-preserving record linkage (PPRL) is a variant of the task in which data owners wish to perform linkage without revealing identifiers associated with the records. This task is desirable in various domains, including healthcare, where it may not be possible to reveal patient identity due to confidentiality requirements, and in business, where it could be disadvantageous to divulge customers' identities. To perform PPRL, it is necessary to apply string comparators that function in the privacy-preserving space. A number of privacy-preserving string comparators (PPSCs) have been proposed, but little research has compared them in the context of a real record linkage application. This paper performs a principled and comprehensive evaluation of six PPSCs in terms of three key properties: (1) correctness of record linkage predictions, (2) computational complexity, and (3) security. We utilize a real publicly-available dataset, derived from the North Carolina voter registration database, to evaluate the tradeoffs between the aforementioned properties. Among our results, we find that PPSCs that partition, encode, and compare strings yield highly accurate record linkage results. However, as a tradeoff, we observe that such PPSCs are less secure than those that map and compare strings in a reduced dimensional space.

Real-world entities are not always represented by the same set of features in different data sets. Therefore, matching records of the same real-world entity distributed across these data sets is a challenging task. If the data sets contain private information, the problem becomes even more difficult. Existing solutions to this problem generally follow two approaches: sanitization techniques and cryptographic techniques. We propose a hybrid technique that combines these two approaches and enables users to trade off between privacy, accuracy, and cost. Our main contribution is the use of a blocking phase that operates over sanitized data to filter out in a privacy-preserving manner pairs of records that do not satisfy the matching condition. We also provide a formal definition of privacy and prove that the participants of our protocols learn nothing other than their share of the result and what can be inferred from their share of the result, their input and sanitized views of the input data sets (which are considered public information). Our method incurs considerably lower costs than cryptographic techniques and yields significantly more accurate matching results compared to sanitization techniques, even when privacy requirements are high. This work was partially supported by Air Force Office of Scientific Research MURI Grant FA9550-08-1-0265, National Institutes of Health Grant 1R01LM009989, US National Science Foundation (NSF) Grant Career-CNS-0845803, and NSF Grants CNS-0964350, CNS-1016343, CNS-1016722 Publisher's Version

Background: Sequential treatment for Helicobacter pylori (H. pylori) appears to achieve a better eradication rate than triple therapy. However, most of the data have been reported from the Italy, and studies from different population are needed before it is recommended in clinical practice. The present study aimed to assess and compare the efficacy of two separate clarithromycin including sequential regimens in Turkey which is well known with high clarithromycin and metronidazole resistance to H. pylori. Methods: Consecutive H. pylori -positive patients with non-ulcer dyspepsia were randomly allocated to one of the two sequential regimens; the first group was given lansoprazole 30 mg b.i.d. plus amoxicillin 1 g b.i.d. for the first week, followed by lansoprazole 30 mg b.i.d., clarithromycin 500 mg b.i.d., and metronidazole 500 mg t.i.d. for the second week (LA-CM). The second arm was given the same regimen but tetracycline500 g q.i.d. instead of metronidazole (LA-CT). H. pylori was detected with urea breath test (UBT) and histology before enrollment. UBT was repeated at 6th weeks after treatment. Results: A total of 200 patients were enrolled in groups and 179 of them completed their protocols. The cumulative per protocol (“PP”) and intention-to-treat (“ITT”) eradication rates were 74.3% and 66.5% in all patients, respectively. Both “PP” (78.2% vs 70.1%) and “ITT” (72% vs 61%) eradication rates were better in LA-CT group than LA-CM group, but the differences were not statistically significant (p > .05). Both regimens were well tolerated, and the incidence of adverse effects was comparable. Conclusion: Two weeks clarithromycin including sequential regimens with metronidazole or tetracycline were not achieved acceptable eradication rates in Turkey.

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.

The biomedical community is increasingly migrating toward research endeavors that are dependent on large quantities of genomic and clinical data. At the same time, various regulations require that such data be shared beyond the initial collecting organization (e.g., an academic medical center). It is of critical importance to ensure that when such data are shared, as well as managed, it is done so in a manner that upholds the privacy of the corresponding individuals and the overall security of the system. In general, organizations have attempted to achieve these goals through deidentification methods that remove explicitly, and potentially, identifying features (e.g., names, dates, and geocodes). However, a growing number of studies demonstrate that deidentified data can be reidentified to named individuals using simple automated methods. As an alternative, it was shown that biomedical data could be shared, managed, and analyzed through practical cryptographic protocols without revealing the contents of any particular record. Yet, such protocols required the inclusion of multiple third parties, which may not always be feasible in the context of trust or bandwidth constraints. Thus, in this paper, we introduce a framework that removes the need for multiple third parties by collocating services to store and to process sensitive biomedical data through the integration of cryptographic hardware. Within this framework, we define a secure protocol to process genomic data and perform a series of experiments to demonstrate that such an approach can be run in an efficient manner for typical biomedical investigations.

Non-alcoholic steatohepatitis (NASH) is closely associated with components of metabolic syndrome. Vaspin is a novel adipocytokine that may link obesity, insulin resistance (IR), and type 2 diabetes mellitus. We aimed to investigate circulating vaspin levels in subjects with NASH and also to search for the association of vaspin with IR, adiponectin, and histological findings.A total of 50 male patients with NASH and 30 healthy male controls were enrolled. Vaspin and adiponectin were measured with ELISA method. Insulin sensitivity determined by homeostasis model assessment (HOMA-IR) index.Plasma vaspin levels were higher and adiponectin levels were lower in NASH group compared with controls (p0.01 and p0.001, respectively). However, in multivariate analysis adjusted for glucose and lipid parameters, and HOMA-IR indexes, the difference in vaspin concentrations was disappeared. Nonetheless, the difference regarding the adiponectin levels remained significant between groups (p = 0.03). Vaspin was negatively correlated with low-density lipoprotein cholesterol (r = -0.32, p = 0.03) in subjects with NASH.This study indicates that circulating vaspin levels are not altered in male subjects with NASH. These results suggest that in the absence of metabolic risk factors, vaspin per se may not be involved in the pathogenesis of NASH.

Background Providers have been reluctant to disclose patient data for public-health purposes. Even if patient privacy is ensured, the desire to protect provider confidentiality has been an important driver of this reluctance. Methods Six requirements for a surveillance protocol were defined that satisfy the confidentiality needs of providers and ensure utility to public health. The authors developed a secure multi-party computation protocol using the Paillier cryptosystem to allow the disclosure of stratified case counts and denominators to meet these requirements. The authors evaluated the protocol in a simulated environment on its computation performance and ability to detect disease outbreak clusters. Results Theoretical and empirical assessments demonstrate that all requirements are met by the protocol. A system implementing the protocol scales linearly in terms of computation time as the number of providers is increased. The absolute time to perform the computations was 12.5 s for data from 3000 practices. This is acceptable performance, given that the reporting would normally be done at 24 h intervals. The accuracy of detection disease outbreak cluster was unchanged compared with a non-secure distributed surveillance protocol, with an F-score higher than 0.92 for outbreaks involving 500 or more cases. Conclusion The protocol and associated software provide a practical method for providers to disclose patient data for sentinel, syndromic or other indicator-based surveillance while protecting patient privacy and the identity of individual providers.

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.

In this paper, the authors discuss security issues for cloud computing and present a layered framework for secure clouds and then focus on two of the layers, i.e., the storage layer and the data layer. In particular, the authors discuss a scheme for secure third party publications of documents in a cloud. Next, the paper will converse secure federated query processing with map Reduce and Hadoop, and discuss the use of secure co-processors for cloud computing. Finally, the authors discuss XACML implementation for Hadoop and discuss their beliefs that building trusted applications from untrusted components will be a major aspect of secure cloud computing.

Organizations, such as federally-funded medical research centers, must share de-identified data on their consumers to publicly accessible repositories to adhere to regulatory requirements. Many repositories are managed by third-parties and it is often unknown if records received from disparate organizations correspond to the same individual. Failure to resolve this issue can lead to biased (e.g., double counting of identical records) and underpowered (e.g., unlinked records of different data types) investigations. In this paper, we present a secure multiparty computation protocol that enables record joins via consumers' encrypted identifiers. Our solution is more practical than prior secure join models in that data holders need to interact with the third party one time per data submission. Though technically feasible, the speed of the basic protocol scales quadratically with the number of records. Thus, we introduce an extended version of our protocol in which data holders append k-anonymous features of their consumers to their encrypted submissions. These features facilitate a more efficient join computation, while providing a formal guarantee that each record is linkable to no less than k individuals in the union of all organizations' consumers. Beyond a theoretical treatment of the problem, we provide an extensive experimental investigation with data derived from the US Census to illustrate the significant gains in efficiency such an approach can achieve.

Even if Role Based Access Control (RBAC) is employed properly, distributed database environments are still prone to illegitimate access attempts: in RBAC, users potentially carry the risk of illegal access attempts via credentials violation, or unintentional/intentional incorrect use of already granted permissions via role misuse/abuse. We introduce a probabilistic risk management model for enhanced access control in such databases. We incorporate failure modes and effects analysis (FMEA) scheme for measuring user risks in our design. We combine components as credentials, queries, role history logs and expected utility for a probabilistic formulation of risk. We present experimental results that we obtained on real world database. The results emphasize the need for a database where roles are well defined and queries under different roles do not overlap. We suggest using query templates with minimized role definitions so that the risk model we introduce provides better risk management.

The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. This is especially important in the field of privacy-preserving data mining (PPDM). Increasing security concerns have led to a surge in work on practical secure multi-party computation protocols. However, most are only proven secure under the semi-honest model, and security under this adversary model is insufficient for many PPDM applications. SMC protocols under the malicious adversary model generally have impractically high complexities for PPDM. We propose an accountable computing (AC) framework that enables liability for privacy compromise to be assigned to the responsible party without the complexity and cost of an SMC-protocol under the malicious model. We show how to transform a circuit-based semi-honest two-party protocol into a protocol satisfying the AC-framework. The transformations are simple and efficient. At the same time, the verification phase of the transformed protocol is capable of detecting any malicious behaviors that can be prevented under the malicious model.

The perturbation method has been extensively studied for privacy preserving data mining. In this method, random noise from a known distribution is added to the privacy sensitive data before the data is sent to the data miner. Subsequently, the data miner reconstructs an approximation to the original data distribution from the perturbed data and uses the reconstructed distribution for data mining purposes. Due to the addition of noise, loss of information versus preservation of privacy is always a trade off in the perturbation based approaches. The question is, to what extent are the users willing to compromise their privacy? This is a choice that changes from individual to individual. Different individuals may have different attitudes towards privacy based on customs and cultures. Unfortunately, current perturbation based privacy preserving data mining techniques do not allow the individuals to choose their desired privacy levels. This is a drawback as privacy is a personal choice. In this paper, we propose an individually adaptable perturbation model, which enables the individuals to choose their own privacy levels. The effectiveness of our new approach is demonstrated by various experiments conducted on both synthetic and real-world data sets. Based on our experiments, we suggest a simple but effective and yet efficient technique to build data mining models from perturbed data.

The problem of sharing manufacturing, inventory, or capacity to improve performance is applicable in many decentralized operational contexts. However, the solution of such problems commonly requires an intermediary or a broker to manage information security concerns of individual participants. Our goal is to examine use of cryptographic techniques to attain the same result without the use of a broker. To illustrate this approach, we focus on a problem faced by independent trucking companies that have separate pick-up and delivery tasks and wish to identify potential efficiency-enhancing task swaps while limiting the information they must reveal to identify these swaps. We present an algorithm that finds opportunities to swap loads without revealing any information except the loads swapped, along with proofs of the security of the protocol. We also show that it is incentive compatible for each company to correctly follow the protocol as well as provide their true data. We apply this algorithm to an empirical data set from a large transportation company and present results that suggest significant opportunities to improve efficiency through Pareto improving swaps. This paper thus uses cryptographic arguments in an operations management problem context to show how an algorithm can be proven incentive compatible as well as demonstrate the potential value of its use on an empirical data set.

Search over encrypted data recently became a critical operation that raised a considerable amount of interest in both academia and industry. Especially, as outsourcing, sensitive data to cloud prove to be a strong trend to benefit from the unmatched storage and computing capacities thereof. Indeed, privacy-preserving search over encrypted data, an apt term to address privacy-related issues concomitant in outsourcing sensitive data, have been widely investigated in the literature under different models and assumptions. In this work, we propose an efficient scheme that allows privacy-preserving search over encrypted data using queries with multiple keywords. Most important contributions of this work are as follows. Firstly, using a property referred as $$\delta $$?-mean query obfuscation, the proposed scheme hides the search patterns, which are allowed to leak in many works in the literature including our preliminary work on the subject Orencik et al. (2013) [1]. Secondly, a two-server setting is employed to eliminate the correlation between the queries and matching documents sent to the user under the assumption that the two servers are not colluding. Thirdly, we propose a novel compression scheme that reduces both the communication cost between the two servers and the computation cost of the search operation more than 55 times compared to the standard approach. And finally, the proposed scheme also provides an effective scoring and ranking capability that is based on term frequency---inverse document frequency (tf-idf) weights of keyword---document pairs. Our analyses demonstrate that the proposed scheme is privacy-preserving, efficient and effective.

Data mining can extract important knowledge from large data collections ut sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. We address secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.

The notion of data provenance was formally introduced a decade ago and has since been investigated, but mainly from a functional perspective, which follows the historical pattern of introducing new technologies with the expectation that security and privacy can be added later. Despite very recent interests from the cyber security community on some specific aspects of data provenance, there is no long-haul, overarching, systematic framework for the security and privacy of provenance. The importance of secure provenance R&D has been emphasized in the recent report on Federal game-changing R&D for cyber security especially with respect to the theme of Tailored Trustworthy Spaces. Secure data provenance can significantly enhance data trustworthiness, which is crucial to various decision-making processes. Moreover, data provenance can facilitate accountability and compliance (including compliance with privacy preferences and policies of relevant users), can be an important factor in access control and usage control decisions, and can be valuable in data forensics. Along with these potential benefits, data provenance also poses a number of security and privacy challenges. For example, sometimes provenance needs to be confidential so it is visible only to properly authorized users, and we also need to protect the identity of entities in the provenance from exposure. We thus need to achieve high assurance of provenance without comprising privacy of those in the chain that produced the data. Moreover, if we expect voluntary large-scale participation in provenance-aware applications, we must assure that the privacy of the individuals or organizations involved will be maintained. It is incumbent on the cyber security community to develop a technical and scientific framework to address the security and privacy challenges so that our society can gain maximum benefit from this technology. In this paper, we discuss a framework of theoretical foundations, models, mechanisms and architectures that allow applications to benefit from privacy-enhanced and secure use of provenance in a modular fashion. After introducing the main components of such a framework and the notion of provenance life cycle, we discuss in details research questions and issues concerning each such component and related approaches.

Sequential treatment for Helicobacter pylori (H. pylori) appears to achieve a better eradication rate than triple therapy. However, most of the data have been reported from the Italy, and studies from different population are needed before it is recommended in clinical practice. The present study aimed to assess and compare the efficacy of two separate clarithromycin including sequential regimens in Turkey which is well known with high clarithromycin and metronidazole resistance to H. pylori.Consecutive H. pylori -positive patients with non-ulcer dyspepsia were randomly allocated to one of the two sequential regimens; the first group was given lansoprazole 30 mg b.i.d. plus amoxicillin 1 g b.i.d. for the first week, followed by lansoprazole 30 mg b.i.d., clarithromycin 500 mg b.i.d., and metronidazole 500 mg t.i.d. for the second week (LA-CM). The second arm was given the same regimen but tetracycline500 g q.i.d. instead of metronidazole (LA-CT). H. pylori was detected with urea breath test (UBT) and histology before enrollment. UBT was repeated at 6th weeks after treatment.A total of 200 patients were enrolled in groups and 179 of them completed their protocols. The cumulative per protocol ("PP") and intention-to-treat ("ITT") eradication rates were 74.3% and 66.5% in all patients, respectively. Both "PP" (78.2% vs 70.1%) and "ITT" (72% vs 61%) eradication rates were better in LA-CT group than LA-CM group, but the differences were not statistically significant (p.05). Both regimens were well tolerated, and the incidence of adverse effects was comparable.Two weeks clarithromycin including sequential regimens with metronidazole or tetracycline were not achieved acceptable eradication rates in Turkey.

Increased levels of serum amylase have been reported in patients with chronic kidney disease (CKD).1 But this elevation is not expected to exceed 2–3 folds of the normal serum levels. More than 2–3...