Protecting Respondents' Identities in Microdata Release

By Pierangela Samarati. 2001

2,521 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Federated Learning: Collaborative Machine Learning without Centralized Training Data (2022, neutral): “The ﬁrst algorithm to achieve k -anonymity was proposed in [23].”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “For example, the well-known property of k-anonymity prevents direct release of information about subgroups with fewer than k individuals [65, 66].”
Privacy Preserving Data Mining (2015, neutral): “Samarati (2001) and Sweeney (2002).”
Anonymizing Classification Data for Privacy Preservation (2007, background): “An example by Samarati [2] shows that linking medication records with a voter list can uniquely identify a person's name and medical information.”
Preserving the Privacy of Sensitive Relationships in Graph Data (2007, methodology): “One of the most popular techniques for anonymizing single table data is k-anonymity, in which the quasi-identifying attributes of the table records are altered in a way that each record becomes indistinguishable from at least k − 1 other records [16].”
Incognito: efficient full-domain K-anonymity (2005, methodology): “However, in many cases, it is likely that users would want the ∞exibility to introduce their own, possibly applicationspeciflc, notions of minimality, as was described in [11, 14 ].”
M-invariance: towards privacy preserving re-publication of dynamic datasets (2007, methodology): “k-anonymity [18, 20] andl-diversity [15] are the two most widely accepted principles, and hence, are used as the representatives in our dis c ssion.”
Anatomy: simple and effective privacy preservation (2006, background): “RELATED WORK Generalization has been very well studied in the literature [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18].”
The new Casper: query processing for location services without compromising privacy (2006, background): “The blurred spatial area can be based either on the k-anonymity concept [Samarati 2001; Sweeney 2002a; 2002b] (i.”
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing (2006, methodology): “The perturbing m ethod [2] and thek-anonymity model [11, 10] are two major techniques for this goal.”
Technical Privacy Metrics (2015, background): “k-Anonymity assumes that identifying columns are removed from a database before publication, and then demands that the database table D can be grouped into equivalence classes with at least k rows that are indistinguishable with respect to their quasi-identifiersq [106, 123].”
Top-down specialization for information and privacy preservation (2005, methodology): “Now, the two classes of the top 24 records become indistinguishable because they are all generalized into <10th, ANY Sex, [1-99)>.”
Context-Aware Generative Adversarial Privacy (2017, background): “Other annoymization techniques, such as generalization [11, 32, 49] and suppression [41, 68, 86], also cannot prevent an adversary from performing the sensitive linkages or recover private information from published datasets [31].”
Privacy Preservation in the Publication of Trajectories (2008, background): “To address this problem, k-anonymity [18, 17] transforms the database before publication, such that the values of attributes in the quasi-identifier are suppressed or generalized in some of the published records.”
Privacy Preserving Image-Based Localization (2019, positive): “Differential privacy [20] and k-anonymity [56] have been applied to the problem of location privacy [3, 6, 26, 72].”
Optimal constraint-based decision tree induction from itemset lattices (2010, methodology): “The idea behind error-based pruning, which was developed as a pruning measure in the C4.5 algorithm (Quinlan, 1993), is to estimate the true error rate of a leaf given the empirical error.”
Computing with Spatial Trajectories (2011, background): “Spatial databases [49, 122, 130] deal with efficient storage and retrieval of objects in space that have identity and well-defined extents, locations, as well as certain geometric and/or topological relationships among them, owing to developments in application fields (GIS, VLSI design, CAD) that ne”