k-anonymity - SAP Help Portal
t-Closeness: Privacy Beyond k-Anonymity and -Diversity least k-1 other records with respect to the quasi-identiﬁer. In other words, k-anonymity requires that each equivalence class contains at least k records. While k-anonymity protects against identity disclosure, it is insufﬁcient to prevent attribute disclosure. To ad-dress this limitation of k-anonymity, Machanavajjhala et al.  recently introduced a new notion of privacy, called ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING So, k-anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. K-Anonymity as a privacy measure - Propiedad Intelectual Jul 11, 2019 GitHub - Nuclearstar/K-Anonymity: Anonymization methods
Apr 28, 2020
To address the privacy issue, we propose a Dummy-Location Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric Privacy-Preserving Big Data Publication: (K, L) Anonymity The focus of this paper is to propose a privacy-preserving approach called (K, L) Anonymity that combines k-anonymity and Laplace differential privacy techniques. This coherent model guarantees privacy from linkage attacks as the risk is mitigated through experimental results. Sensitive Attributes based Privacy Preserving in Data
k-anonymity is a property of a data set, usually used in order to describe the data set’s level of anonymity. A dataset is k-anonymous if every combination of identity-revealing characteristics occurs in at least k different rows of the data set.
and the k-anonymity model [11, 10] are two major techniques for this goal. The k-anonymity model has been extensively studied recently because of its relative conceptual simplicity and effective-ness (e.g. [5, 1]). In this paper, we focus on a study on the k-anonymity prop-erty [11, 10]. The k-anonymity model assumes a quasi-identiﬁer, t-Closeness: Privacy Beyond k-Anonymity and l-Diversity Apr 20, 2007 Achieving k-anonymity in privacy-aware location-based To address the privacy issue, we propose a Dummy-Location Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric