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Large genomic datasets are created through numerous activities, including recreational genealogical investigations, biomedical research, and clinical care. At the same time, genomic data has become valuable for reuse beyond their initial point of collection, but privacy concerns often hinder access. Beacon services have emerged to broaden accessibility to such data. These services enable users to query for the presence of a particular minor allele in a dataset, and information helps care providers determine if genomic variation is spurious or has some known clinical indication. However, various studies have shown that this process can leak information regarding if individuals are members of the underlying dataset. There are various approaches to mitigate this vulnerability, but they are limited in that they (1) typically rely on heuristics to add noise to the Beacon responses; (2) offer probabilistic privacy guarantees only, neglecting data utility; and (3) assume a batch setting where all queries arrive at once. In this article, we present a novel algorithmic framework to ensure privacy in a Beacon service setting with a minimal number of query response flips. We represent this problem as one of combinatorial optimization in both the batch setting and the online setting (where queries arrive sequentially). We introduce principled algorithms with both privacy and, in some cases, worstcase utility guarantees. Moreover, through extensive experiments, we show that the proposed approaches significantly outperform the state of the art in terms of privacy and utility, using a dataset consisting of 800 individuals and 1.3 million single nucleotide variants. [ABSTRACT FROM AUTHOR]

The quantity of personal data that is collected, stored, and subsequently processed continues to grow rapidly. Given its sensitivity, ensuring privacy protections has become a necessary component of database management. To enhance protection, a number of mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities. However, this approach is limited. First, the volume of alerts is often substantially greater than the auditing capabilities of organizations. Second, strategic attackers can attempt to disguise their actions or carefully choose targets, thus hide illicit activities. In this article, we introduce an auditing approach that accounts for adversarial behavior by (1) prioritizing the order in which types of alerts are investigated and (2) providing an upper bound on how much resource to allocate for each type. Specifically, we model the interaction between a database auditor and attackers as a Stackelberg game. We show that even a highly constrained version of such problem is NP-Hard. Then, we introduce a method that combines linear programming, column generation, and heuristic searching to derive an auditing policy. On the synthetic data, we perform an extensive evaluation on the approximation degree of our solution with the optimal one. The two real datasets, (1) 1.5 months of audit logs from Vanderbilt University Medical Center and (2) a publicly available credit card application dataset, are used to test the policy-searching performance. The findings demonstrate the effectiveness of the proposed methods for searching the audit strategies, and our general approach significantly outperforms non-game-theoretic baselines. [ABSTRACT FROM AUTHOR]

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highlydetailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward. [ABSTRACT FROM AUTHOR]

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach.

Collaborative information systems (CIS) are deployed within a diverse array of environments, ranging from the Internet to intelligence agencies to healthcare. It is increasingly the case that such systems are applied to manage sensitive information, making them targets for malicious insiders. While sophisticated security mechanisms have been developed to detect insider threats in various file systems, they are neither designed to model nor to monitor collaborative environments in which users function in dynamic teams with complex behavior. In this paper, we introduce a community-based anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on information recorded in the access logs of collaborative environments. CADS is based on the observation that typical users tend to form community structures, such that users with low a nity to such communities are indicative of anomalous and potentially illicit behavior. The model consists of two primary components: relational pattern extraction and anomaly detection. For relational pattern extraction, CADS infers community structures from CIS access logs, and subsequently derives communities, which serve as the CADS pattern core. CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies. To empirically evaluate the threat detection model, we perform an analysis with six months of access logs from a real electronic health record system in a large medical center, as well as a publicly-available dataset for replication purposes. The results illustrate that CADS can distinguish simulated anomalous users in the context of real user behavior with a high degree of certainty and with significant performance gains in comparison to several competing anomaly detection models.

Regulations in various countries permit the reuse of health information without patient authorization provided the data is "de-identified". In the United States, for instance, the Privacy Rule of the Health Insurance Portability and Accountability Act defines two distinct approaches to achieve de-identification; the first is Safe Harbor , which requires the removal of a list of identifiers and the second is Expert Determination , which requires that an expert certify the re-identification risk inherent in the data is sufficiently low. In reality, most healthcare organizations eschew the expert route because there are no standardized approaches and Safe Harbor is much simpler to interpret. This, however, precludes a wide range of worthwhile endeavors that are dependent on features suppressed by Safe Harbor, such as gerontological studies requiring detailed ages over 89. In response, we propose a novel approach to automatically discover alternative de-identification policies that contain no more re-identification risk than Safe Harbor. We model this task as a lattice-search problem, introduce a measure to capture the re-identification risk, and develop an algorithm that efficiently discovers polices by exploring the lattice. Using a cohort of approximately 3000 patient records from the Vanderbilt University Medical Center, as well as the Adult dataset from the UCI Machine Learning Repository, we also experimentally verify that a large number of alternative policies can be discovered in an efficient manner.