M-invariance: towards privacy preserving re-publication of dynamic datasets

By Xiaokui Xiao, Yufei Tao. ACM SIGMOD Conference, 2007

578 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ed375a8e1f23b452124fdc124403bf9731de88b8

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Citations in the corpus

Privacy integrated queries (2010, background): “Unfortunately, sanitization is neither easy to do, nor even to define; research in the area has yet to stabilize on robust definitions that come without known vulnerabilities [7, 8, 9 ]. Furthermore, these approaches intentionally contort the data before release, requiring the analyst to understand”
Privacy integrated queries: an extensible platform for privacy-preserving data analysis (2009, background): “Unfortunately, sanitization is neither easy to do, nor even to define; research in the area has yet to stabilize on robust definitions that come without known vulnerabilities [7, 8, 9].”
Minimality Attack in Privacy Preserving Data Publishing (2007, methodology): “We also have examples to show the feasibility of minimality attacks on the algorithms for (c, k)-safety in [13], Personalized Privacy in [24], and sequential releases in [18] and [25].”
K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, background): “Obviously, if G is a k -automorphic network, for any vertex v in G , we cannot distinguish v from its k − 1 symmetric vertices based on any structural information.”
Airavat: Security and Privacy for MapReduce (2010, background): “Modifications of k-anonymity, such asl-diversity [31] andminvariance [46], suffer from many of the same flaws.”
The cost of privacy: destruction of data-mining utility in anonymized data publishing (2008, background): “Limitations of k-anonymity are: (1) it does not hide whether a given individual is in the database [26,30], (2) it reveals individuals’ sensitive attributes [21, 22], (3) it does not protect against attacks based on background knowledge [22, 23], (4) mere knowledge of the k-anonymization algorithm c”
Privacy-preserving data publishing: A survey of recent developments (2010, background): “2007], m-invariance [Xiao and Tao 2007], and (X, Y )-privacy [Wang and Fung 2006].”
Technical Privacy Metrics (2015, background): “ improvement to k-anonymity, it has been shown to oer insucient protection against some attacks. In particular, it does not protect privacy when multiple releases of statistical data are available [Xiao and Tao 2007], when the distribution of sensitive values is skewed [Li et al. 2007], or when sens”
Differential Privacy: A Survey of Results (2008, unknown): “…and from such diverse branches of computer science as algorithms, database theory, and cryptography , for example as in [3] [4] [21] [22] [23] [33] [34] [41] [48], [1] [24] [25] [31], and [6] [9] [11] [12] [13] [18] [7] [19]; see also the survey [2] for a summary of the field prior to 1989.”
Resisting structural re-identification in anonymized social networks (2008, background): “, m-invariance [49] defends against the composition of multiple releases of a dynamic table), developing data publication techniques that resist all of them is an open problem, not only for graph data, but for tabular data as well.”
An Ad Omnia Approach to Defining and Achieving Private Data Analysis (2007, background): “However, Xiao and Tao [43] note that multiple -diverse data releases completely compromise privacy.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “While the work in [127] explores the issue of sequential releases from the point of view of adding additional attributes, the work in [134] discusses the”