Broadening the Scope of Differential Privacy Using Metrics

By Konstantinos Chatzikokolakis, Miguel E. Andrés, Nicolás Emilio Bordenabe, Catuscia Palamidessi. 2013

458 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Privacy & Security

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Citations in the corpus

Certified Robustness to Adversarial Examples with Differential Privacy (2018, background): “Previous work has also shown that DP can be broadened to metrics other than the Hamming distance [8], [14].”
Bayesian Differential Privacy through Posterior Sampling (2013, methodology): “This generalisation has also been used by Chatzikokolakis et al. (2013), which has laid the groundwork for metric-based differential privacy.”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, methodology): “easily preserve real-time continuously varying data from distributed linear devices by using the metric method presented in [311].”
Geo-indistinguishability: differential privacy for location-based systems (2012, methodology): “Our proposal is based on a generalization of differential privacy [14] developed in [8].”
Differentially private distributed convex optimization via objective perturbation (2016, background): “Also of relevance to our paper is [19], which proposes the use of metrics instead of binary adjacency relations for differential privacy.”
Entropy-minimizing mechanism for differential privacy of discrete-time linear feedback systems (2014, methodology): “In this work, we study the relationship between privacy and accuracy in the framework of a discrete-time feedback control system, where accuracy is measured by Shannon entropy and a version of -differential privacy without adjacency is adopted [11] (see Section II).”
Combining Differential Privacy and Secure Multiparty Computation (2015, background): “Differential privacy has been generalized to take into account that different records or columns of a database may have different sensitivity [8].”
Blowfish privacy: tuning privacy-utility trade-offs using policies (2013, background): “We discuss variants of differential privacy [6] (including restricted sensitivity [1]), the Pufferfish framework [13], privacy axioms [11], and a recent independent work on extending differential privacy with metrics [3].”
Local Differential Private Data Aggregation for Discrete Distribution Estimation (2019, methodology): “As a metric generalization of the original -LDP in [23] for capturing intrinsic structures of values in general discrete data, the -LDP in Definition 1 of this work is also known as -geoindistinguishability [17], dX -privacy [18] or data obfuscation [18], [19], [22], [25].”
Element Level Differential Privacy: The Right Granularity of Privacy (2019, unknown): “The key to differential privacy and its descendant deﬁnitions is the notion of neighboring datasets [Dwork and Roth 2014] or samples, where privacy guarantees certify that an adversary given the output of a private mechanism M cannot reliably distinguish between its applications M ( x ) and M ( x…”
Metrics for Differential Privacy in Concurrent Systems (2014, background): “In [7], the principle of differential privacy has been formally extended to measure the degree of protection of secrets in more general settings.”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “However, this is not applicable in a variety of settings [21, 5], which leads to new and extended notions such as δ-neighborhood [13] or event-level [11] differential privacy.”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “This concept can be naturally extended to any set of secrets X , equipped with a metric dX [15, 12].”
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics (2017, background): “Chatzikokolakis et al [17] investigate notions of privacy that can be deﬁned as metrics over the set of databases.”
Technical Privacy Metrics (2015, methodology): “d-χ -Privacy is a generalization of differential privacy that uses distinguishability metrics dχ to characterize the distance between two datasets instead of the Hamming distance used in standard differential privacy [20].”
A Survey on Differential Privacy for Unstructured Data Content (2022, methodology): “Metric DP has extended Hamming-distance-based DP to arbitrary metric-space-based privacy notions [11].”
Differentially Private Distributed Convex Optimization via Functional Perturbation (2015, methodology): “The authors of [20] also employ metrics instead of binary adjacency relations in the context of differential privacy.”
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy (2023, methodology): “A more continuous relaxation called dχ-privacy was proposed by Chatzikokolakis et al. (2013).”