Download Data privacy: principles and practice by Nataraj Venkataramanan, Ashwin Shriram PDF

By Nataraj Venkataramanan, Ashwin Shriram

The ebook covers facts privateness extensive with admire to facts mining, try out info administration, man made information new release and so forth. It formalizes rules of information privateness which are crucial for reliable anonymization layout in keeping with the information structure and self-discipline. the foundations define top practices and consider the conflicting dating among privateness and software. From a convention viewpoint, it offers practitioners and researchers with a definitive consultant to technique anonymization of varied info codecs, together with multidimensional, longitudinal, time-series, transaction, and graph facts. as well as aiding CIOs guard exclusive info, it additionally bargains a tenet as to how this is often applied for quite a lot of info on the company level.

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Encrypted data have high protection, but are not readable, so how can we use such data? Another issue associated with cryptography is key management. Any compromise of key means complete loss of privacy. For the use cases discussed in this book, cryptographic techniques are not used widely. Of course, there are techniques like secure multiparty computation (MPC) and zero-knowledge proof (ZKP), which are discussed in detail in Duan and Canny [3]. Anonymization is a set of techniques used to modify the original data in such a manner that it does not resemble the original value but maintains the semantics and syntax.

We discuss this in sufficient detail in Chapter 4. The utility loss of a particular anonymization technique is measured against the utility provided by the original data set. 7 Anonymized Table with Generalized Values—Correlation between QI and SD Is Broken Name Yang Emma Olivia Krishna Zip Code Gender Income 56000 56010 56000 56010 Male Female Female Male 25K 8K 30K 5K 18 Data Privacy: Principles and Practice is also the correlation between QI and SD preserved in the anonymized data. There are many anonymization techniques in use today, which can be broadly classified into perturbative and nonperturbative techniques.

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. 6. Let us not worry about the techniques used to anonymize data, but focus just on the results. We can see that the names have been changed, the original ZIP CODES have been replaced with different values and INCOME values are unchanged. Let us assess gains and losses for this anonymization design.

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