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267
Differential Privacy and Private Bayesian Inference‹
"... We consider a Bayesian statistician (B) communicating with an untrusted third party (A). B wants to convey useful answers to the queries of A, but with-out revealing private information. For example, we may want to give statistics about how many people suffer from a disease, but without revealing wh ..."
Abstract
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We consider a Bayesian statistician (B) communicating with an untrusted third party (A). B wants to convey useful answers to the queries of A, but with-out revealing private information. For example, we may want to give statistics about how many people suffer from a disease, but without revealing
B.: Robust and private Bayesian inference
"... Abstract. We examine the robustness and privacy of Bayesian infer-ence, under assumptions on the prior, and with no modications to the Bayesian framework. First, we generalise the concept of differential pri-vacy to arbitrary dataset distances, outcome spaces and distribution fam-ilies. We then prov ..."
Abstract
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Cited by 4 (3 self)
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Abstract. We examine the robustness and privacy of Bayesian infer-ence, under assumptions on the prior, and with no modications to the Bayesian framework. First, we generalise the concept of differential pri-vacy to arbitrary dataset distances, outcome spaces and distribution fam-ilies. We
On Differentially Private Inductive Logic Programming
"... Abstract. We consider differentially private inductive logic program-ming. We begin by formulating the problem of guaranteeing differential privacy to inductive logic programming, and then prove the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While ..."
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Abstract. We consider differentially private inductive logic program-ming. We begin by formulating the problem of guaranteeing differential privacy to inductive logic programming, and then prove the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While
Managing Distributed, Shared L2 Caches through OS-Level Page Allocation
- IEEE/ACM INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE
, 2006
"... This paper presents and studies a distributed L2 cache management approach through OS-level page allocation for future many-core processors. L2 cache management is a crucial multicore processor design aspect to overcome non-uniform cache access latency for good program performance and to reduce on-c ..."
Abstract
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Cited by 134 (11 self)
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hardware support. Furthermore, our approach can provide differentiated execution environment to running programs by dynamically controlling data placement and cache sharing degrees. We discuss key design issues of the proposed approach and present preliminary experimental results showing the promise of our
Privbayes: Private data release via bayesian networks
- SIGMOD
, 2014
"... ABSTRACT Privacy-preserving data publishing is an important problem that has been the focus of extensive study. The state-of-the-art goal for this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. Existing ..."
Abstract
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Cited by 4 (0 self)
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, which renders the published data next to useless. To address the deficiency of the existing methods, this paper presents PRIVBAYES, a differentially private method for releasing high-dimensional data. Given a dataset D, PRIVBAYES first constructs a Bayesian network N , which (i) provides a succinct
Differentially-private Learning and Information Theory
"... Using results from PAC-Bayesian bounds in learning theory, we formulate differentially-private learning in an information theoretic framework. This, to our knowledge, is the first such treatment of this increasingly popular notion of data privacy. We examine differential privacy in the PAC-Bayesian ..."
Abstract
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Cited by 1 (0 self)
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Using results from PAC-Bayesian bounds in learning theory, we formulate differentially-private learning in an information theoretic framework. This, to our knowledge, is the first such treatment of this increasingly popular notion of data privacy. We examine differential privacy in the PAC-Bayesian
Information-theoretic bounds for differentially private mechanisms
- In 24rd IEEE Computer Security Foundations Symposium, CSF 2011. IEEE Computer Society, Los Alamitos
"... Abstract—There are two active and independent lines of research that aim at quantifying the amount of information that is disclosed by computing on confidential data. Each line of research has developed its own notion of confidentiality: on the one hand, differential privacy is the emerging consensu ..."
Abstract
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Cited by 25 (2 self)
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of confidentiality, and to compare them in terms of the security guarantees they deliver. We obtain the following results. First, we establish upper bounds for the leakage of every ɛ-differentially private mechanism in terms of ɛ and the size of the mechanism’s input domain. We achieve this by identifying
L.: Bayesian inference under differential privacy
"... Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can b ..."
Abstract
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Cited by 1 (0 self)
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Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can
Coalitional Bayesian Nash implementation in differential information economies, mimeo
- University of Illinois at Urbana-Champaign
, 1995
"... Summary. A mechanism coalitionally implements a social choice set if any out-come of the social choice set can be achieved as a coalitional Bayesian Nash equilibrium of a mechanism and vice versa. We say that a social choice set is coalitionally implementable if there is a mechanism which coalitiona ..."
Abstract
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Cited by 2 (0 self)
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condition. As an application of our main result, we show that the private core and the private Shapley value of an economy with differential information are coalitionally implementable.
A workflow for differentially-private graph synthesis
, 2012
"... We present a new workflow for differentially-private publication of graph topologies. First, we produce differentiallyprivate measurements of interesting graph statistics using our new version of the PINQ programming language, Weighted PINQ, which is based on a generalization of differential privacy ..."
Abstract
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Cited by 3 (1 self)
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We present a new workflow for differentially-private publication of graph topologies. First, we produce differentiallyprivate measurements of interesting graph statistics using our new version of the PINQ programming language, Weighted PINQ, which is based on a generalization of differential
Results 1 - 10
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267
