teras offshore，oh麦卡满为什么不免配送_专业问答

t-closeness t-closeness?is a further refinement of? l-diversity?group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or mining algorithms in order to gain some privacy. The? t-closeness model extends the?l-diversity model by treating the values of an attribute distinctly by taking into account the distribution of data values for that attribute. 归纳起来感觉就是给予对相应属性的具体内容分布进行有区分的 Attacks on?l-diversity This is useful because in real data sets attribute values may be skewed or semantically similar. However, accounting for value distributions may cause difficulty in creating feasible?l-diverse representations. The?l-diversity technique is useful in that it may hinder an attacker leveraging the global distribution of an attribute's data values in order to infer information about sensitive data values. Not every value may exhibit equal sensitivity, for example, a rare positive indicator for a disease may provide more information than a common negative indicator. Because of examples like this,?l-diversity may be difficult and unnecessary to achieve when protecting against attribute disclosure. Alternatively, sensitive information leaks may occur because while?l-diversity requirement ensures 便宜美国vps “diversity” of sensitive values in each group, it does not recognize that values may be semantically close, for example, an attacker could deduce a stomach disease applies to an individual if a sample containing the individual only listed three different stomach diseases. Formal definition[edit] Given the existence of such attacks where sensitive attributes may be inferred based upon the distribution of values for?l-diverse data, the?t-closeness method was created to further?l-diversity by additionally maintaining the distribution of sensitive fields. The original paper[1]?by xndxs, 迷路的可乐, and?jzddb?defines?t-closeness as:

The?t-closeness Principle:?An equivalence class is said to have?t-closeness if the distance between the distribution of a sensitive attribute in this class and the distribution of the attribute in the whole table is no more than a threshold?t. A table is said to have?t-closeness if all equivalence classes have?t-closeness.

References[edit] Jump up^?xndxs, 迷路的可乐, and jzddb (2007).?"t-Closeness: Privacy beyond?k-anonymity and?l-diversity"?(PDF).?ICDE. Purdue University.?doi:10.1109/ICDE.2007.367856.Jump up^?Charu C. Aggarwal; Philip S. Yu, eds. (2008). "A General Survey of Privacy".?Privacy-Preserving Data Mining – Models and Algorithms?(PDF). Springer.?ISBN?978-0-387-70991-8. 60499892

teras offshore，oh麦卡满为什么不免配送

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