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Yaakobi Eitan - One of the best experts on this subject based on the ideXlab platform.
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Multi-Server Weakly-Private Information Retrieval
2021Co-Authors: Lin Hsuan-yin, Rosnes Eirik, Kumar Siddhartha, Amat, Alexandre Graell, Yaakobi EitanAbstract:Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the servers storing the database. While existing protocols strictly impose that no information is leaked on the file's identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the perfect Privacy Requirement. We refer to such protocols as weakly-private information retrieval (WPIR) protocols. In particular, for the case of multiple noncolluding replicated servers, we study how the download rate, the upload cost, and the access complexity can be improved when relaxing the full Privacy constraint. To quantify the information leakage on the requested file's identity we consider mutual information (MI), worst-case information leakage, and maximal leakage (MaxL). We present two WPIR schemes, denoted by Scheme A and Scheme B, based on two recent PIR protocols and show that the download rate of the former can be optimized by solving a convex optimization problem. We also show that Scheme A achieves an improved download rate compared to the recently proposed scheme by Samy et al. under the so-called $\epsilon$-Privacy metric. Additionally, a family of schemes based on partitioning is presented. Moreover, we provide an information-theoretic converse bound for the maximum possible download rate for the MI and MaxL Privacy metrics under a practical restriction on the alphabet size of queries and answers. For two servers and two files, the bound is tight under the MaxL metric, which settles the WPIR capacity in this particular case. Finally, we compare the performance of the proposed schemes and their gap to the converse bound.Comment: To appear in IEEE Transactions on Information Theory. arXiv admin note: text overlap with arXiv:1901.0673
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Multi-Server Weakly-Private Information Retrieval
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Lin, Hsuan Yin, Rosnes Eirik, Kumar Siddhartha, Graell Amat I Alexandre, Yaakobi EitanAbstract:Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the servers storing the database. While existing protocols strictly impose that no information is leaked on the file’s identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the perfect Privacy Requirement. We refer to such protocols as weakly-private information retrieval (WPIR) protocols. In particular, for the case of multiple noncolluding replicated servers, we study how the download rate, the upload cost, and the access complexity can be improved when relaxing the perfect Privacy constraint. To quantify the information leakage on the requested file’s identity we consider mutual information (MI), worst-case information leakage, and maximal leakage (MaxL). We present two WPIR schemes, denoted by Scheme A and Scheme B, based on two recent PIR protocols and show that the download rate of the former can be optimized by solving a convex optimization problem. We also show that Scheme A achieves an improved download rate compared to the recently proposed scheme by Samy et al. under the so-called ϵ-Privacy metric. Additionally, a family of schemes based on partitioning is presented. Moreover, we provide an information-theoretic converse bound for the maximum possible download rate for the MI and MaxL Privacy metrics under a practical restriction on the alphabet size of queries and answers. For two servers and two files, the bound is tight under the MaxL metric, which settles the WPIR capacity in this particular case. Finally, we compare the performance of the proposed schemes and their gap to the converse bound
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Multi-Server Weakly-Private Information Retrieval
2020Co-Authors: Lin Hsuan-yin, Rosnes Eirik, Kumar Siddhartha, Amat, Alexandre Graell, Yaakobi EitanAbstract:Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the servers storing the database. While existing protocols strictly impose that no information is leaked on the file's identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the perfect Privacy Requirement. We refer to such protocols as weakly-private information retrieval (WPIR) protocols. In particular, for the case of multiple noncolluding replicated servers, we study how the download rate, the upload cost, and the access complexity can be improved when relaxing the full Privacy constraint. To quantify the information leakage on the requested file's identity we consider mutual information (MI), worst-case information leakage, and maximal leakage (MaxL). We present two WPIR schemes based on two recent PIR protocols and show that the download rate of the former can be optimized by solving a convex optimization problem. Additionally, a family of schemes based on partitioning is presented. Moreover, we provide an information-theoretic converse bound for the maximum possible download rate for the MI and MaxL Privacy metrics under a practical restriction on the alphabet size of queries and answers. For two servers and two files, the bound is tight under the MaxL metric, which settles the WPIR capacity in this particular case. Finally, we compare the performance of the proposed schemes and their gap to the converse bound.Comment: Submitted to IEEE for possible publication. arXiv admin note: text overlap with arXiv:1901.0673
Ludger Goeke - One of the best experts on this subject based on the ideXlab platform.
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a catalog of security Requirements patterns for the domain of cloud computing systems
ACM Symposium on Applied Computing, 2014Co-Authors: Kristian Beckers, Isabelle Cote, Ludger GoekeAbstract:Security and Privacy concerns are essential in cloud computing scenarios, because cloud customers and end customers have to trust the cloud provider with their critical business data and even their IT infrastructure. In projects these are often addressed late in the software development life-cycle, because these are difficult to elicit in cloud scenarios, due to the large amount of stakeholders and technologies involved. We contribute a catalog of security and Privacy Requirement patterns that support software engineers in eliciting these Requirements. As Requirements patterns provide artifacts for re-using Requirements. This paper shows how these Requirements can be classified according to cloud security and Privacy goals. Furthermore, we provide a structured method on how to elicit the right Requirements for a given scenario. We mined these Requirements patterns from existing security analysis of public organizations such as ENISA and the Cloud Security Alliance, from our practical experience in the cloud domain, and from our previous research in cloud security. We validate our Requirements patterns in co-operation with industrial partners of the ClouDAT project.
Shuheng Zhou - One of the best experts on this subject based on the ideXlab platform.
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a statistical framework for differential Privacy
Journal of the American Statistical Association, 2010Co-Authors: Larry Wasserman, Shuheng ZhouAbstract:One goal of statistical Privacy research is to construct a data release mechanism that protects individual Privacy while preserving information content. An example is a random mechanism that takes an input database X and outputs a random database Z according to a distribution Qn(⋅|X). Differential Privacy is a particular Privacy Requirement developed by computer scientists in which Qn(⋅|X) is required to be insensitive to changes in one data point in X. This makes it difficult to infer from Z whether a given individual is in the original database X. We consider differential Privacy from a statistical perspective. We consider several data-release mechanisms that satisfy the differential Privacy Requirement. We show that it is useful to compare these schemes by computing the rate of convergence of distributions and densities constructed from the released data. We study a general Privacy method, called the exponential mechanism, introduced by McSherry and Talwar (2007). We show that the accuracy of this meth...
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a statistical framework for differential Privacy
arXiv: Statistics Theory, 2008Co-Authors: Larry Wasserman, Shuheng ZhouAbstract:One goal of statistical Privacy research is to construct a data release mechanism that protects individual Privacy while preserving information content. An example is a {\em random mechanism} that takes an input database $X$ and outputs a random database $Z$ according to a distribution $Q_n(\cdot|X)$. {\em Differential Privacy} is a particular Privacy Requirement developed by computer scientists in which $Q_n(\cdot |X)$ is required to be insensitive to changes in one data point in $X$. This makes it difficult to infer from $Z$ whether a given individual is in the original database $X$. We consider differential Privacy from a statistical perspective. We consider several data release mechanisms that satisfy the differential Privacy Requirement. We show that it is useful to compare these schemes by computing the rate of convergence of distributions and densities constructed from the released data. We study a general Privacy method, called the exponential mechanism, introduced by McSherry and Talwar (2007). We show that the accuracy of this method is intimately linked to the rate at which the probability that the empirical distribution concentrates in a small ball around the true distribution.
Eric D Peterson - One of the best experts on this subject based on the ideXlab platform.
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supporting open access to clinical trial data for researchers the duke clinical research institute bristol myers squibb supporting open access to researchers initiative
American Heart Journal, 2016Co-Authors: Michael J Pencina, Darcy M Louzao, Brian Mccourt, Monique R Adams, Rehbar H Tayyabkhan, Peter Ronco, Eric D PetersonAbstract:There are growing calls for sponsors to increase transparency by providing access to clinical trial data. In response, Bristol-Myers Squibb and the Duke Clinical Research Institute have collaborated on a new initiative, Supporting Open Access to Researchers. The aim is to facilitate open sharing of Bristol-Myers Squibb trial data with interested researchers. Key features of the Supporting Open Access to Researchers data sharing model include an independent review committee that ensures expert consideration of each proposal, stringent data deidentification/anonymization and protection of patient Privacy, Requirement of prespecified statistical analysis plans, and independent review of manuscripts before submission for publication. We believe that these approaches will promote open science by allowing investigators to verify trial results as well as to pursue interesting secondary uses of trial data without compromising scientific integrity.
Nagabhushana Prabhu - One of the best experts on this subject based on the ideXlab platform.
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accuracy constrained Privacy preserving access control mechanismfor relational data
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Zahid Pervaiz, Walid G Aref, Arif Ghafoor, Nagabhushana PrabhuAbstract:Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized user can still compromise the Privacy of a person leading to identity disclosure. A PPM can use suppression and generalization of relational data to anonymize and satisfy Privacy Requirements, e.g., k-anonymity and l-diversity, against identity and attribute disclosure. However, Privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained Privacy-preserving access control framework. The access control policies define selection predicates available to roles while the Privacy Requirement is to satisfy the k-anonymity or l-diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art.