Hiding the presence of individuals from shared databases

By Mehmet Ercan Nergiz, Maurizio Atzori, Chris Clifton. ACM SIGMOD Conference, 2007

362 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c3581b50b98416c7ebda9d8c5d87c2d853b39762

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Citations in the corpus

Slicing: A New Approach to Privacy Preserving Data Publishing (2009, background): “Existing privacy measures for membership disclosure protection include diﬀerential privacy [7, 8, 9] and δ - presence [27].”
Differential identifiability (2012, background): “…related approaches prevent the adversary from uniquely identifying an individual’s record, they assume little prior knowledge available to the adversary. δpresence [18] protects against determining if an individual is in a dataset even if the adversary has full knowledge of values of individuals.”
Airavat: Security and Privacy for MapReduce (2010, background): “It does not hide whether a particular individual is in the dataset [42], nor the sensitive attributes associated with any individual [32, 34].”
Preserving the Privacy of Sensitive Relationships in Graph Data (2007, background): “More definitions of privacy and information disclosure can be found in [4] [5] [10] [11].”
Technical Privacy Metrics (2015, background): “In databases, δ -presence bounds the adversary’s probability of inferring that an individual u is part of some published data DY , assuming that the adversary has access to external database tables DZ so that all individuals in DY are also in DZ [98]: privDLP ≡ (δmin ,δmax ), where ∀u ∈ UZ : δmin ≤ ”
On the tradeoff between privacy and utility in data publishing (2009, background): “, linking an individual to a record in the database) and membership disclosure [7, 23] (i.”
Trustworthy AI: From Principles to Practices (2021, background): “Different principles of quantitative data anonymization have been developed, such as k-anonymity [288], (c,k )-safety [236], and δ -presence [253].”
Privacy-preserving data publishing: A survey of recent developments (2010, background): “Presence Algorithms SPALM and MPALM: Recall that a generalized table T I satis.es (dmin,dmax)-presence (or simply d-presence) with respect to an external table E if dmin = P(t .”
Towards trajectory anonymization: a generalization-based approach (2008, background): “(E.g., an example of such applications is studied in [ 37 ].”