Toward Privacy in Public Databases

By S. Chawla, Cynthia Dwork, Frank McSherry, Adam D. Smith, Hoeteck Wee. 2005

325 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy (2012, background): “Differential privacy was developed through a series of papers [8], [11], [6], [4].”
Big healthcare data: preserving security and privacy (2018, methodology): “These methods have a common problem of difficulty in anonymizing high dimensional data sets [32, 33].”
Preserving the Privacy of Sensitive Relationships in Graph Data (2007, background): “To formalize privacy preservation, Chawla et al. [4] propose a framework based on the intuitive definition that " our privacy is protected to the extent we blend in the crowd.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “For general analysis purposes, information loss metrics are defined to measure the similarity between the original data and the protected data [23].”
Distributed Differential Privacy and Applications (2015, background): “, 2006b) census Aggregation 9 / 50 4 (Chawla et al., 2005) kmeans Clustering 46 / 148 6 (Blum et al.”
Differential Privacy (2006, unknown): “…for the interactive approach have been obtained ( [13, 3, 12] and the present paper), while the non-interactive case has proven to be more diﬃcult, (see [14, 4, 5] ), possibly due to the diﬃculty of supplying utility that has not yet been speciﬁed at the time the sanitization is carried out.”
Worst-Case Background Knowledge for Privacy-Preserving Data Publishing (2007, background): “In many cases, the fact that a particular individual is in the table is considered sensitive information [8].”
Crowd-Blending Privacy (2012, background): “(Another definition partly based on the intuition of blending in a crowd is (c, t)isolation [18], which requires adversaries to be unable to isolate an individual, represented by a data point in R, by roughly determining the individual’s location in R; we formalize the intuition of blending in a cro”
L-diversity: Privacy beyond k-anonymity (2007, unknown): “[1-10], [11-20], etc), we would end up with very large q∗-blocks.”
Cryptography from Anonymity (2006, background): “These works examine privacy through “blending in with the crowd” [14], obtained via the perturbation of data revealed to the adversary.”
Differential Privacy for Statistics: What we Know and What we Want to Learn (2010, background): “We were unable to prove the statement in the presence of arbitrary auxiliary information [6].”
Our Data, Ourselves: Privacy Via Distributed Noise Generation (2006, unknown): “accurate statistics about a population while preserving the privacy of individuals [1, 2, 15, 11, 14, 5, 6, 4, 13].”
Injecting utility into anonymized datasets (2006, background): “For example, [16] studies how to reconstruct association rules after noise has been added; [5] and [4] study how to reconstruct the distribution of a continuous variable after noise with a known distribution has been added; [9] studies how to perturb the values of continuous numeric attributes so th”
The cost of privacy: destruction of data-mining utility in anonymized data publishing (2008, background): “Techniques include adding random noise to the data while preserving certain statistical aggregates [4,8, 13] and interactive output perturbation [6,11].”