By Nataraj Venkataramanan, Ashwin Shriram
The ebook covers facts privateness extensive with appreciate to facts mining, attempt facts administration, man made facts iteration and so forth. It formalizes rules of information privateness which are crucial for sturdy anonymization layout according to the knowledge layout and self-discipline. the rules define top practices and give some thought to the conflicting dating among privateness and application. From a tradition perspective, it presents practitioners and researchers with a definitive advisor to process anonymization of varied information codecs, together with multidimensional, longitudinal, time-series, transaction, and graph information. as well as assisting CIOs safeguard private information, it additionally bargains a tenet as to how this is often applied for a variety of info on the company level.
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Additional resources for Data privacy: principles and practice
Cryptographic mechanism provides low utility (0) and high privacy (1) when data are encrypted and it provides high utility (1) and low privacy (0) when data are decrypted. The privacy or utility in a cryptographic mechanism is either black (0) or white (1), whereas in anonymization methods, it is “shades of gray,” meaning that is possible to control the levels of privacy or utility. Anonymization can be viewed as constrained optimization—produce a data set with smallest distortion that also satisfies the given set of privacy requirements.
What methods are available to protect sensitive data? Some of the methods available are cryptography, anonymization, and tokenization, which are briefly discussed in this section, and a detailed coverage is provided in the other chapters in the book. Of course, there are other one-way functions like hashing. Cryptographic techniques are probably one of the oldest known techniques for data protection. When done right, they are probably one of the safest techniques to protect data in motion and at rest.
To support this, we felt it is necessary to define a set of design principles. These principles will provide the required guidelines for the data anonymizer to adopt the correct design for a given anonymization requirement. As software architects, we start the architecting process by following a set of architecture principles that will guide us to come up with the correct design for the system. We base our work here on a similar approach. In , the authors classify principles into two broad types—scientific and normative.