Corpus ID: 2181340. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression @inproceedings{Samarati1998ProtectingPW, title={Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression}, author={Pierangela Samarati and Latanya Sweeney}, year={1998} }
Challenges and techniques in Big data security and privacy Mar 22, 2018 ANONYMIZATION BASED ON NESTED CLUSTERING FOR … But the privacy is provided to an extent except closeness attack. The most popular algorithms for the data mining research community in this category are k-anonymity and ℓ-diversity. K-anonymity requires each tuple in the published table to be indistinguishable from at least k-1 other tuples. Privacy Preservation Techniques | SpringerLink
parameter to determine the privacy degree is the size of the anonymity set, for example, k-anonymity [3] (or variations like l-diversity [18] and t-closeness [19]), which tries to hide the real information of a user into other k-1 users. Based on this model, a number of follow-up works appear, such as [8], [18].
In recent years, a new definitionof privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain “identifying” attributes. ILLIA: Enabling $k$ -Anonymity-Based Privacy Preserving The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users.
User autonomy + privacy suggest a decentralized infrastructure SIGMOD, June 2010 * Make safer to join and post data for publishers Prevent association of sensitive topics with publishers that contribute to them even if compromised nodes Publisher k-anonymity: For every publisher p and data item d, hide p in a k-protected crowd of publishers
However, unencrypted data provides no guarantee for anonymity. In order to preserve privacy, k-anonymity model has been proposed by Sweeney [5] which achieves k- anonymity using generalization and suppression [5], In K- anonymity, it is difficult for an imposter to decide the identity of the individuals in Privacy-Preserving Access Control Mechanism for Relational