Computational Differential Privacy

By Ilya Mironov, Omkant Pandey, Omer Reingold, Salil P. Vadhan. 2009

299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Privacy & Security

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In Artificial Intelligence

In Computational Theory and Mathematics

In Privacy & Security

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “As such, virtually all implementations of differential privacy satisfy only (variants of) computational differential privacy introduced by [298].”
A General Approach to Adding Differential Privacy to Iterative Training Procedures (2018, methodology): “As such, virtually all implementations of differential privacy satisfy only (variants of) Computational Differential Privacy introduced by [Mironov et al., 2009].”
Rényi Differential Privacy (2017, background): “Under the indistinguishability-based Computational Differential Privacy (IND-CDP) definition [3], the test of closeness between distributions on adjacent inputs is computationally bounded (all other definitions considered in this paper hold against an unbounded, information-theoretic adversary).”
Boosting and Differential Privacy (2010, background): “(This can be viewed as an information-theoretic analogue of the “dense model theorems” of [15], [25], [21], [20].”
Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “Barthe et al. [5] present a system for reasoning about computational differential privacy [39] a relaxation of differential privacy where the adversary are computationally-bound.”
Distributed Private Data Analysis: On Simultaneously Solving How and What (2008, methodology): “Following our work, computational differential privacy was considered in [21]; they present several definitions of computational differential privacy, study the relationships between these definitions, and construct efficient 2-party computational differentially private protocols for approximating t”
PrivEx: Private Collection of Traffic Statistics for Anonymous Communication Networks (2014, background): “Mironov et al. [26] investigate calculating the distance between two vectors while McGregor et al. [24] do the same for Hamming distance.”
Differential Privacy by Typing in Security Protocols (2013, background): “, verifiable secret sharing [6], secure function evaluations [7], secure multi-party computations [8], multi-party distributed data”
Local Differential Private Data Aggregation for Discrete Distribution Estimation (2019, background): “, in SMC) and distributed noise generation to collaboratively preserve computational differential privacy [11] for a group of users, but their”
Lossless Compression of Efficient Private Local Randomizers (2021, background): “Computational Differential Privacy (CDP) (Mironov et al., 2009) is a notion of privacy that defends against computationally bounded adversaries.”
DJoin: Differentially Private Join Queries over Distributed Databases (2012, unknown): “In contrast, DJoin provides computational differential privacy [25]: it relies on a homomorphic cryptosystem and thus depends on certain computational hardness assumptions.”
Verified Computational Differential Privacy with Applications to Smart Metering (2013, methodology): “This section presents these extensions.”
The Algorithmic Foundations of Differential Privacy (2014, background): “A formal study of computational differential privacy was initiated in [64], and the separation between the accuracy that can be achieved with (ε, 0)-differential privacy in the multiparty and single curator cases in Theorem 9.”
Practical and Robust Privacy Amplification with Multi-Party Differential Privacy (2019, background): “[30] lays the theoretical foundation of the relationship among several kinds of computational DP deﬁnitions.”
The Limits of Two-Party Differential Privacy (2010, unknown): “The “catch” (and the reason this does not contradict our aforementioned result on the Hamming distance) is that this approach only achieves a relaxed notion of computational differential privacy [11].”
Probabilistic relational reasoning for differential privacy (2012, methodology): “An immediate objective for future work is to use the gameplaying technique from [3] for verifying in CertiPriv the privacy of multi-party computation algorithms, where one is concerned with ensuring privacy against (computationally bounded) adversaries that only have a partial view of the state, con”
Differential Privacy for Statistics: What we Know and What we Want to Learn (2010, methodology): “Are there other techniques? Mironov, Pandey, Reingold, and Vadhan have recently found an example of a cryptographic protocol (as opposed to statistical database implementation) in which moving to computational differential privacy permits much smaller distortion [45].”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, background): “There is a separation between the algorithmic power of computational DP and information-theoretic DP in the multi-party setting [79].”