Privacy integrated queries: an extensible platform for privacy-preserving data analysis

By Frank McSherry. 2009

1,479 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Privacy & Security

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Citations in the corpus

Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “There is now a substantial literature on differential privacy, both on the theoretical foundations [28] and on practical implementations [20, 55, 62, 68].”
AdaCliP: Adaptive Clipping for Private SGD (2019, methodology): “To keep track of accumulated privacy loss over multiple iterations of SGD, a privacy accountant is used [32].”
Detecting Violations of Differential Privacy (2018, methodology): “Programming platforms, such as PINQ [29], Airavat [37], and GUPT [32] provide a small set of primitive operations that can be used as building blocks of algorithms for differential privacy.”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, methodology): “By leveraging multiparty computations, they create virtual trusted-third-party platforms similar to the centralized PINQ or FLEX systems, which support differentially-private release of analysis results about user data [38, 52].”
Rényi Differential Privacy (2017, methodology): “We define group privacy using a notion of c-stable transformation [20].”
Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “To this end, we propose both the sequential composition theorem and the parallel composition theorem for DDP by extending the previous results on composition for DP in [30].”
Learning privately from multiparty data (2016, methodology): “Differential privacy has been used for a privacy-preserving data analysis platform (McSherry, 2009), and for sanitization of learned model parameters from a standard ERM (Chaudhuri et al., 2011).”
Privacy via the Johnson-Lindenstrauss Transform (2012, methodology): “We adopt a rigorous approach to privacy introduced by Dwork et al. [12], which has gained widespread recognition in recent years (see a survey [10]), and has been used to demonstrate the feasibility of privacy-preserving data releases [3, 20, 23, 24].”
Differentially Private Model Publishing for Deep Learning (2019, neutral): “For -DP, the similar result in [30] of the parallel composition theorem says that when each idifferentially private mechanism queries a disjoint subset of data in parallel and work independently, their composition provides (maxi i)-DP instead of the ∑ i i derived from a naive composition.”
Proving Differential Privacy in Hoare Logic (2014, methodology): “WhilePINQ is restricted to ǫ-differential privacy,Airavat can handle also approximate differential privacy using a runtime monitor forδ.”
The matrix mechanism: optimizing linear counting queries under differential privacy (2015, background): “The vector form of the Laplace and Gaussian mechanisms above incorporateswhat is sometimes referred to as parallel composition for answering multiple queries [23].”
How Much Is Enough? Choosing ε for Differential Privacy (2011, background): “PINQ [12] is an implementation of differential privacy that provides answers to SQL queries in a differentially private way.”
A Survey on Differential Privacy for Unstructured Data Content (2022, methodology): “Concerning some statistical queries, such as objects’ density and stay time in certain places, the Video DP scheme is compared with the conventional privacy integrated queries system [65] and demonstrates better utility.”
PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy (2018, methodology): “We utilize the sequential composition property of LDP [6], as follows.”
The optimal mechanism in differential privacy (2012, neutral): “, [10], [11], [12], [13], [14], [5], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46].”