k-Anonymity: A Model for Protecting Privacy

By Latanya Sweeney. 2002

9,049 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Sociology and Political Science, Safety, Robustness & Interpretability

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Citations in the corpus

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (2017, background): “Numerous linkage and membership inference attacks on both biomedical datasets [6-13] and from machine learning models [14-16] have demonstrated the ability to re-identify participants or reveal participation in a study.”
Deep Learning with Differential Privacy (2016, background): “Another approach, k-anonymity and closely related notions [54], seeks to offer a degree of protection to underlying data by generalizing and suppressing certain identifying attributes.”
Calibrating Noise to Sensitivity in Private Data Analysis (2006, background): “We note that k-anonymity [15] and the similarly motivated notion of protection against isolation [7] [8]) have also been in the eye of privacy research.”
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, background): “For instance, k-anonymity requires information about an individual to be indistinguishable from at least k − 1 other individuals in the dataset (L. Sweeney, 2002).”
Privacy in Deep Learning: A Survey (2020, methodology): “This method was used for protecting patients while processing medical data and has been shown to fail on many occasions [49, 19, 50].”
Preserving ON-OFF Privacy for Past and Future Requests (2019, methodology): “Several existing privacy-preserving solutions can be utilized to ensure a desired level of privacy for the user, such as anonymity [1], differential privacy [2], private information retrieval [3], to name a few.”
ON-OFF Privacy with Correlated Requests (2019, background): “This is now a well-acknowledged concern and has lead to many interesting theoretical problems such as anonymity [1], differential privacy [2], private information retrieval [3], and other privacy-preserving algorithms.”
Federated Machine Learning (2019, neutral): “malicious assumptions. These works require participants’ data to be secretly-shared among non-colluding servers. DifferentialPrivacy.AnotherlineofworkusetechniquesDifferentialPrivacy[18]ork-Anonymity [63] for data privacy protection [1, 12, 42, 61]. The methods of differential privacy, k-anonymity, ”
Federated learning of predictive models from federated Electronic Health Records (2018, background): “In particular, even though maintaining all data in a central location enables the implementation of anonymization measures (e.g., k-anonymity [13]), it introduces a single point of attack or failure and makes it possible for a data breach to expose identifiable data for many individuals.”
Signal Processing and Machine Learning with Differential Privacy (2013, background): “For example, the adversary could use publicly available records such as voting polls [3].”
Differentially Private Password Frequency Lists (2016, background): “a) Privacy Models: De-anonymization attacks [28], [35] highlight the problems with heuristic approaches to data sanitization (e.”
Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Data privacy is an issue of critical importance, motivating perturbation of query results over sensitive datasets for protecting users’ privacy [5], [12], [26], [29], [31], [38].”
Learning privately from multiparty data (2016, methodology): “To preserve privacy in data publishing, several approaches such as k-anonymity (Sweeney, 2002) and secure multiparty computation (Yao, 1982) have been proposed (see (Fung et al.”
Security Evaluation of Support Vector Machines in Adversarial Environments (2014, background): “Indeed the notion of differential privacy, as opposed to more syntactic notions of privacy such as k-anonymity [64], was inspired by decades-old work in cryptography that introduced mathematical formalism to an age-old problem, yielding”
Differentially Private Empirical Risk Minimization (2009, neutral): “There has also been a significant amount of work on privacy-preserving data mining (Agrawal and Srikant, 2000; Evfimievski et al., 2003; Sweeney, 2002; Machanavajjhala et al., 2006), spanning several communities, that uses privacy models other than differential privacy.”
A Statistical Framework for Differential Privacy (2008, methodology): “Disclosure limitation methods include clustering (Sweeney 2002; Aggarwal et al. 2006), -diversity (Machanavajjhala et al. 2006), t-closeness (Li, Li, and Venkatasubramanian 2007), data swapping (Fienberg and McIntyre 2004), matrix masking (Ting, Fienberg, and Trottini 2008), cryptographic…”
Composition attacks and auxiliary information in data privacy (2008, methodology): “Partition-based schemes include k -anonymity [30] as well as several recent variants, e.g., [26, 23, 36, 27, 9].”
Efficient Private Statistics with Succinct Sketches (2015, methodology): “Work in [24] and [43] provide either k -anonymity [63] and l -diversity [50] to guarantee anonymity of users through Mix Network techniques [17].”