By Nataraj Venkataramanan, Ashwin Shriram
The ebook covers info privateness extensive with admire to info mining, try out information administration, artificial info new release and so forth. It formalizes rules of information privateness which are crucial for stable anonymization layout in accordance with the information layout and self-discipline. the rules define most sensible practices and think about the conflicting courting among privateness and software. From a tradition viewpoint, it offers practitioners and researchers with a definitive advisor to process anonymization of varied information codecs, together with multidimensional, longitudinal, time-series, transaction, and graph facts. as well as aiding CIOs guard private info, it additionally deals a suggestion as to how this is applied for a variety of facts on the company level.
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Additional info for Data privacy: principles and practice
Anonymization is an optimization problem, in that when the original data are modified they lose some of its utility. But modification of the data is required to protect it. An anonymization design is a balancing act between data privacy and utility. Privacy goals are set by the data owners, and utility goals are set by data users. Now, is it really possible to optimally achieve this balance between privacy and utility? We will explore this throughout this book. Tokenization is a data protection technique that has been extensively used in the credit card industry but is currently being adopted in other domains as well.
Anonymized data are utilized in many areas of an organization like data mining, analysis, or creating test data. An important point to remember here is each type of requirement or analysis warrants a different anonymization design. This means that there is no single privacy versus utility measure. 6. 1. 5 Privacy versus utility map. 6 shows four individuals. Although many rows have not been shown here, let us assume that the ZIP CODE and INCOME are correlated, in that the ZIP CODE 56001 primarily consists of high-income individuals.
For example, a healthcare provider’s database could contain how patients have reacted to a particular drug or treatment. This information would be useful to a pharmaceutical company. However, these sensitive data cannot be shared or released due to legal, financial, compliance, and moral issues. But for the benefit of the organization and the customer, there is a need to share these data responsibly, which means the data are shared without revealing the PII of the customer. 2 Sensitive data in the enterprise.