By Nataraj Venkataramanan, Ashwin Shriram
The publication covers information privateness extensive with admire to facts mining, attempt facts administration, man made facts iteration and so on. It formalizes ideas of knowledge privateness which are crucial for stable anonymization layout in line with the knowledge structure and self-discipline. the foundations define top practices and consider the conflicting dating among privateness and application. From a tradition point of view, it offers practitioners and researchers with a definitive advisor to procedure anonymization of varied facts codecs, together with multidimensional, longitudinal, time-series, transaction, and graph facts. as well as aiding CIOs defend personal information, it additionally deals a tenet as to how this is often carried out for a variety of information on the firm level.
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Additional info for Data privacy: principles and practice
Consider a personal loan application in a bank. It has over two hundred fields that need to be filled in by the applicant. Most of these fields happen to be QI. Such a high number of fields in QI proves a major challenge for a nonymization. PPDM is exploratory in nature, and irrespective of which anonymization technique is used, there is bound to be high information loss or low utility. With high-dimensional data, it becomes difficult to define a clear boundary between QI and SD as we do not know how much background knowledge an adversary has.
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Data privacy: principles and practice by Nataraj Venkataramanan, Ashwin Shriram