2009-4-15 · k-Anonymity • Each released record should be indistinguishable from at least (k-1) others on its QI attributes • Alternatively: cardinality of any query result on released data should be at least k • k-anonymity is (the first) one of many privacy definitions in this line of work – l-diversity, t-closeness, m-invariance, delta-presence
2018-8-13 · 差分隐私保护是目前非常热的课题，一篇中文综述送给大家 9 3203 2010 X 3.1 contingency table 2005 Chawla 2006 2011 Dwork K-means 2005 Bli Blu um K→ neaRs n K 22 Hardt 2011 Dwork K—mea n log k log Y I d+1 1/ d+1N/ 4 log A log X d+1c14 2012 2008 Chaud A Statistical Approach to Provide Individualized Privacy In this paper we propose an instrument for collecting sensitive data that allows for each participant to customize the amount of information that she is comfortable revealing. Current methods adopt a uniform approach where all subjects are afforded the same privacy guarantees; however, privacy is a highly subjective property with intermediate points between total disclosure and non-disclosure Challenges of Enterprise Cloud Services1 | SpringerLink Abstract. In traditional IT organizations, the operator has complete control of and visibility into their service offering and infrastructure. All components are accessible and can be measured by the enterprise’s set of well known tools.
2015-3-27 · [LLV07] Li N, Li T, Venkatasubramanian S. t-Closeness: privacy beyond k-anonymity and l-diversity. ICDE; p. 106–15. 2007. [LLZ+12] Li T, Li N, Zhang J, Molloy I. Slicing: A new approach for privacy preserving data publishing. IEEE Trans Knowl Data Eng, 24(3):561–574, 2012 [LM12] Li C, Miklau G. An adaptive mechanism for accurate query
t-Closeness: Privacy beyond k-Anonymity and l-Diversity
Tuning anonymity level for assuring high data quality: an
T-closeness - Cohen Courses 2020-6-12 · Both k-anonymity and l-diversity have a number of limitations. These privacy definitions are neither necessary nor sufficient to prevent attribute disclosure, particularly if the distribution of sensitive attributes in an equivalence class do not match the distribution of sensitive attributes in the whole data set. t-closeness addresses this problem by requiring that the distribution of a An overview of methods for data anonymization 2015-3-27 · [LLV07] Li N, Li T, Venkatasubramanian S. t-Closeness: privacy beyond k-anonymity and l-diversity. ICDE; p. 106–15. 2007. [LLZ+12] Li T, Li N, Zhang J, Molloy I. Slicing: A new approach for privacy preserving data publishing. IEEE Trans Knowl Data Eng, 24(3):561–574, 2012 [LM12] Li C, Miklau G. An adaptive mechanism for accurate query PPT – Privacy and anonymity PowerPoint presentation | free