2015
DOI: 10.1016/j.ins.2014.11.005
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Privacy by diversity in sequential releases of databases

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Cited by 35 publications
(20 citation statements)
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“…These approaches were improved in [14] with the guarantee that an adversary cannot link any quasi-identifier tuple with any sensitive value with probability greater than 1/ . Their application scenario is of sequential release publishing in which the set of tuples is fixed, while the set of attributes changes from one release to another.…”
Section: Related Literaturementioning
confidence: 99%
“…These approaches were improved in [14] with the guarantee that an adversary cannot link any quasi-identifier tuple with any sensitive value with probability greater than 1/ . Their application scenario is of sequential release publishing in which the set of tuples is fixed, while the set of attributes changes from one release to another.…”
Section: Related Literaturementioning
confidence: 99%
“…For instance, they assume the data to be released in a single table, completely available for anonymization before release, and never republished. However, it may happen that data are either republished over time or continuously generated, as in the case with data streams: recent proposals (e.g., Fung et al 2008;Loukides et al 2013;Shmueli and Tassa 2015;Shmueli et al 2012;Tai et al 2014;Xiao and Tao 2007) have extended traditional approaches to deal with these scenarios.…”
Section: Extensions For Advanced Scenariosmentioning
confidence: 99%
“…We also assume that the adversary acquired this knowledge for all data subjects in the table T ; as stated earlier, such a strong assumption is also very common, e.g. [20,35,50,51,56,61,62]. Hence, the adversary has, on one hand, the projection of T onto its first M attributes, and on the other hand the published table T in which the first M attributes are perturbed, and it includes additional L attributes that could be sensitive.…”
Section: Classical Utility and Privacy Measuresmentioning
confidence: 99%
“…For the sake of achieving a better privacy guarantee, a more widely accepted adversarial assumption (see, e.g. [20,35,50,51,56,61,62]) is a stronger one: the adversary knows the set of individuals who contributed their information to the database and was able to extract their quasi-identifier information from publicly available databases. Such an adversary knows the projection of T on its quasi-identifier attributes.…”
Section: A Global Dbrl Disclosure Risk Measurementioning
confidence: 99%
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