Privacy integrated queries

By Frank McSherry. 2010

212 citations recorded by the source corpus.

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

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Citations in the corpus

Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “…for providing formal veriﬁcation of the differential privacy guarantee, using a wide variety of veriﬁcation approaches: dynamic checking [24, 36], relational program logic [2, 6] and relational reﬁnement type systems [9], linear (dependent) type systems [29, 42], product programs [7],…”
Proving Differential Privacy via Probabilistic Couplings (2016, neutral): “The seminal result of McSherry and Talwar (2007) establishes differential privacy for this mechanism.”
Signal Processing and Machine Learning with Differential Privacy (2013, background): “…privacy for discrete data—in particular, we will not discuss some of the progress made in software systems engineering for differential privacy [25]–[27], algorithms for computing histo grams and contingency tables [28], [29], or the large body of work on privacypreserving data release…”
DJoin: Differentially Private Join Queries over Distributed Databases (2012, unknown): “Differentially private query processors: PINQ [23], Airavat [32], and Fuzz [16] are query processors that support differential privacy, but they assume a centralized setting in which a single entity has access to the entire data.”
Computational Differential Privacy (2009, background): “The definition of group privacy is often applicable when the differentially-private mechanism is preceded by computations that may amplify (up to a constant) the number of records affected by any individual [19].”
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy (2014, background): “Pierce [46] defines three categories: run-time enforcement (PINQ [37], AIRAVAT [50]), static enforcement (Fuzz [48], DFuzz [24]), and verification-based enforcement (CertiPriv [3]).”
Monitoring web browsing behavior with differential privacy (2014, background): “The privacy parameter α, also called the privacy budget [21], specifies the degree of privacy offered.”
K-variates++: More Pluses in the K-means++ (2016, methodology): “The performance of k-means based on PINQ relies on two principal factors: the initialisation (like in the non differentially private version) and the number of iterations.”
Probabilistic Inference and Differential Privacy (2010, background): “The heuristic gradient ascent algorithm has an important configuration parameter determining the number of iterations of ascent, and consequently the accuracy permitted in each round (which must be lower if more rounds are to be run, to keep the cumulative privacy cost constant).”
Fault-Tolerant Privacy-Preserving Statistics (2012, background): “In the PINQ framework [18] the aggregate knowledge gain of the attacker, i.”
DStress: Efficient Differentially Private Computations on Distributed Data (2017, background): “Distributed query processors: The first differentially private query processors, such as PINQ [49], Fuzz [38], and Airavat [61] assumed a centralized setting in which the analyst has access to all the private data.”
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale (2020, background): “In this less restrictive privacy setting, the main industrial differential privacy systems include Microsoft’s PINQ [6], Uber’s FLEX for its internal analytics [7], LinkedIn’s PriPeARL for its ad analytics [8], Google’s recent differential privacy open source project [9, 10], and the 2020 U.”
Differentially private data release for data mining (2011, methodology): “Professional [18-65) Professional [18-65) Professional [18-65) Professional [18-65)”
Differential Privacy Under Fire (2011, methodology): “Differential privacy: There is a considerable body of work on the theory of differential privacy [8–10] and on differentially private data analysis [20, 26].”
Distributed Differential Privacy and Applications (2015, methodology): “…manual proofs by privacy experts that the answers to particular queries were safe to release (McSherry and Mironov, 2009); today, systems like PINQ (McSherry, 2009) and Airavat (Roy et al., 2010) can perform differentially private data analysis automatically, without needing a human expert in the…”