Data Privacy: Principles and Practice by Nataraj Venkataramanan, Ashwin Shriram

By Nataraj Venkataramanan, Ashwin Shriram

The ebook covers information privateness intensive with appreciate to information mining, attempt facts administration, artificial info new release and so on. It formalizes rules of knowledge privateness which are crucial for sturdy anonymization layout according to the knowledge layout and self-discipline. the foundations define most sensible practices and think of the conflicting courting among privateness and software. From a tradition viewpoint, it offers practitioners and researchers with a definitive consultant to strategy anonymization of varied info codecs, together with multidimensional, longitudinal, time-series, transaction, and graph facts. as well as supporting CIOs guard exclusive facts, it additionally bargains a tenet as to how this is applied for quite a lot of information on the firm level.

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The two designs also show that privacy or utility need not be 0 and 1 as in encryption; rather, both are shades of gray as stated earlier. A good design can achieve a balance between them and achieve both goals to a reasonable extent. One way to quantify privacy is on the basis of how much information an adversary can obtain about the SD of an individual from different dimensions in the data set [5–8]. These references state that SD fields can be identified (or estimated/deduced) using QI fields.

8. Bob is the adversary and has some background information about Alice as she is his neighbor. Bob knows that Alice smokes heavily but does not really know what disease she is suffering from. However, he has knowledge about the distribution of the sensitive fields in a table containing medical records of a hospital that he has noticed Alice visiting. Bob then uses the knowledge of the distribution of SD fields and background information about Alice to identify her illness, which is cancer. 2 Measuring Utility of Anonymized Data Assume that in the original data D, QI, and SD are highly correlated.

3 illustrates an example of the substitution method, in which the names in the original data table for the North American region are substituted with similar names obtained from an external data source. On the Internet, we can get name databases pertaining to different countries and regions. For example, one can obtain a database containing Spanish names, English names, or Indian Names. As their names are fictitious, they could be safely substituted in place of original names, thus protecting the privacy of the record owner.

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