Noiseless Database Privacy

By Raghav Bhaskar, Abhishek Bhowmick, Vipul Goyal, Srivatsan Laxman, Abhradeep Thakurta. 2011

102 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Differentially private data aggregation with optimal utility (2014, background): “In this work we focus on the notion of differential privacy, but for completeness, we refer to some recent papers that investigate limitations of this notion [37,43] and propose alternative definitions of privacy for statistical queries [15,33,42].”
Differential Privacy by Typing in Security Protocols (2013, background): “[35] and propose alternative notions of privacy for queries on statistical databases, such as relaxations of differential privacy [36], noiseless definitions [37], and zero-knowledge based definitions for social networks [38].”
On significance of the least significant bits for differential privacy (2012, background): “…privacy has been explored in the work of Bhaskar et al., which taps into uncertainty that the adversary may have about the input into the database [1]; Blocki et al. describe a randomized functionality (the Johnson-Lindenstrauss transform) that oﬀers a relaxation of diﬀer-ential privacy without…”
Satisfiability modulo counting: a new approach for analyzing privacy properties (2014, background): “Thishas led to a set of privacy and conﬁdentiality deﬁni-tions [11, 18, 20, 35, 40, 42, 46] that give precise notions of acceptable disclosure or partial information leakage .”
Cardinality Estimators do not Preserve Privacy (2018, background): “However, this is not so obvious for our deﬁnition of privacy: prior work [7, 11, 28] shows that when the attacker is assumed to have some uncertainty about the data, even deterministic algorithms can satisfy the corresponding deﬁnition of privacy.”
Stadium: A Distributed Metadata-Private Messaging System (2017, background): “As suggested in [2, 5, 24], Stadium can benefit from using legitimate users’ traffic as noise.”
Pufferfish (2014, background): “P uffer F ish( S , pairs ) satisﬁes the axioms of convexity and transformation invariance.”
Testing the Lipschitz Property over Product Distributions with Applications to Data Privacy (2013, methodology): “Speciﬁcally, we adapt a particular relaxation from [3] and show that it can indeed be tested using a connection to testing the Lipschitz property.”
Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy (2013, background): “We also explore a specific instantiation of the framework that we call distributional differential privacy, and illustrate its applicability by studying several appealing “noiseless” mechanisms satisfying the resulting definition.”
Vuvuzela: scalable private messaging resistant to traffic analysis (2015, unknown): “In a system with many users, it may be reasonable to assume that some fraction of users are honest (i.e., the adversary does not know what they are doing), formalized with the help of coupled-worlds privacy [3] or noiseless database privacy [5].”