Differential Privacy

By Hamid Ebadi, David Sands, Gerardo Schneider. 2015

77 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management

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Citations in the corpus

Detecting Violations of Differential Privacy (2018, background): “Several dynamic tools [21, 29, 37, 39, 40] exist for enforcing differential privacy.”
LightDP: towards automating differential privacy proofs (2016, background): “However, this example clearly demonstrates the potential benefits of explicitly calculating the privacy cost in the target language.”
Individual Privacy Accounting via a Renyi Filter (2020, methodology): “Our individual accounting relies on an RDP version of personalized differential privacy [13].”
Combining Differential Privacy and Secure Multiparty Computation (2015, methodology): “We report on the experience that we have obtained with the implementations of GUPT’s sample-and-aggregate method [31] and the provenance for PDP method [14] on top of the Sharemind SMC framework.”
Personalised Differential Privacy Summary of POPL ’ 15 paper “ Differential Privacy : Now It ’ s Getting Personal ” (2015, background): “Personalised Differential Privacy This paper addresses these issues by offering a simple generalisation of differential privacy called personalised differential privacy (PDP) [4] which permits each individual to have a personalised privacy budget.”
Fast Differentially Private Matrix Factorization (2015, background): “Lastly, we recently become aware that personalized privacy (the exact same definition) has been independently developed in [10] along with a few basic properties that mirrors the standard DP.”
Per-instance Differential Privacy (2017, background): “The closest existing definition to ours is perhaps the personalized-DP, first seen in Ebadi et al. (2015); Liu et al. (2015).”
Proving differential privacy with shadow execution (2019, background): “ses itself on standard program logics, which makes the transformed program re-usable by other program analyses. Other language-based proofs Recent work such as Personalized Differential Privacy (PDP) [23] allows each individual to set its own different privacy level and PDP will satisfy difference p”