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Results 1 - 10
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160
Publishing SetValued Data via Differential Privacy
"... Set-valued data provides enormous opportunities for various data mining tasks. In this paper, we study the problem of publishing set-valued data for data mining tasks under the rigorous differential privacy model. All existing data publishing methods for set-valued data are based on partitionbased p ..."
Abstract
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Cited by 31 (13 self)
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trees. We propose a probabilistic top-down partitioning algorithm to generate a differentially private release, which scales linearly with the input data size. We also discuss the applicability of our idea to the context of relational data. We prove that our result is (ǫ,δ)-useful for the class
Modular reasoning about differential privacy in a probabilistic process calculus
- In TGC
, 1982
"... Abstract. The verification of systems for protecting sensitive and confidential information is becoming an increasingly important issue. Differential privacy is a promising notion of privacy originated from the community of statistical databases, and now widely adopted in various models of computati ..."
Abstract
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Cited by 4 (0 self)
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of computation. We con-sider a probabilistic process calculus as a specification formalism for concurrent systems, and we propose a framework for reasoning about the degree of differ-ential privacy provided by such systems. In particular, we investigate the preser-vation of the degree of privacy under
Probabilistic anonymity via coalgebraic simulations
- European Symposium on Programming (ESOP 2007), volume 4421 of Lect. Notes Comp. Sci
, 2007
"... Abstract. There is a growing concern on anonymity and privacy on the Internet, resulting in lots of work on formalization and verification of anonymity. Especially, importance of probabilistic aspect of anonymity is claimed recently by many authors. Among them are Bhargava and Palamidessi who presen ..."
Abstract
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Cited by 8 (3 self)
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Abstract. There is a growing concern on anonymity and privacy on the Internet, resulting in lots of work on formalization and verification of anonymity. Especially, importance of probabilistic aspect of anonymity is claimed recently by many authors. Among them are Bhargava and Palamidessi who
Differentially private empirical risk minimization
, 2010
"... Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical ris ..."
Abstract
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Cited by 77 (6 self)
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Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical
Metrics for Differential Privacy in Concurrent Systems
"... Abstract. Originally proposed for privacy protection in the context of statisti-cal databases, differential privacy is now widely adopted in various models of computation. In this paper we investigate techniques for proving differential pri-vacy in the context of concurrent systems. Our motivation s ..."
Abstract
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Cited by 1 (1 self)
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Abstract. Originally proposed for privacy protection in the context of statisti-cal databases, differential privacy is now widely adopted in various models of computation. In this paper we investigate techniques for proving differential pri-vacy in the context of concurrent systems. Our motivation
NProbabilistic Relational Reasoning for Differential Privacy
"... Differential privacy is a notion of confidentiality that allows useful computations on sensible data while protecting the privacy of individuals. Proving differential privacy is a difficult and error-prone task that calls for principled approaches and tool support. Approaches based on linear types a ..."
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Differential privacy is a notion of confidentiality that allows useful computations on sensible data while protecting the privacy of individuals. Proving differential privacy is a difficult and error-prone task that calls for principled approaches and tool support. Approaches based on linear types
Image Sequence Analysis via Partial Differential Equations
, 1999
"... This article deals with the problem of restoring and motion segmenting noisy image sequences with a static background. Usually, motion segmentation and image restoration are considered separately in image sequence restoration. Moreover, motion segmentation is often noise sensitive. In this article, ..."
Abstract
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Cited by 50 (3 self)
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, the motion segmentation and the image restoration parts are performed in a coupled way, allowing the motion segmentation part to positively influence the restoration part and vice-versa. This is the key of our approach that allows to deal simultaneously with the problem of restoration and motion segmentation
The Geometry of Differential Privacy: The Sparse and Approximate Cases
, 2012
"... In this work, we study trade-offs between accuracy and privacy in the context of linear queries over histograms. This is a rich class of queries that includes contingency tables and range queries, and has been a focus of a long line of work [BLR08,RR10,DRV10,HT10,HR10,LHR+10,BDKT12]. For a given set ..."
Abstract
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Cited by 16 (5 self)
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) approximation guarantee for the case of (ε, δ)-differential privacy. Our mechanism is simple, efficient and adds carefully chosen correlated Gaussian noise to the answers. We prove its approximation guarantee relative to the hereditary discrepancy lower bound of [MN12], using tools from convex geometry. We next
Beyond Differential Privacy: Composition Theorems and Relational Logic for f-divergences between Probabilistic Programs
"... Abstract. f-divergences form a class of measures of distance between probability distributions; they are widely used in areas such as information theory and signal processing. In this paper, we unveil a new connection between f-divergences and differential privacy, a confidentiality policy that prov ..."
Abstract
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Cited by 1 (0 self)
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composition theorem of differential privacy. Then, we propose a relational program logic to prove upper bounds for the f-divergence between two probabilistic programs. Our results allow us to revisit the foundations of differential privacy under a new light, and to pave the way for applications that use
Probabilistic Anonymity via Coalgebraic Simulations 1
"... There is a growing concern about anonymity and privacy on the Internet, result-ing in lots of work on formalization and verification of anonymity. Especially, the importance of probabilistic aspect of anonymity is claimed recently by many au-thors. Several different notions of “probabilistic anonymi ..."
Abstract
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There is a growing concern about anonymity and privacy on the Internet, result-ing in lots of work on formalization and verification of anonymity. Especially, the importance of probabilistic aspect of anonymity is claimed recently by many au-thors. Several different notions of “probabilistic
Results 1 - 10
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160
