Privacy requirements have an increasing impact on the
realization of modern applications. Commercial and legal
regulations demand that privacy guarantees be provided whenever
sensitive information is stored, processed, or communicated to
external parties. Current approaches encrypt sensitive data, thus
reducing query execution efficiency and preventing selective
information release.
Preserving Privacy in Data Outsourcing presents a comprehensive
approach for protecting highly sensitive information when it is
stored on systems that are not under the data owner's control. The
approach illustrated combines access control and encryption,
enforcing access control via structured encryption. This solution,
coupled with efficient algorithms for key derivation and
distribution, provides efficient and secure authorization
management on outsourced data, allowing the data owner to outsource
not only the data but the security policy itself. To reduce the
amount of data to be encrypted the book also investigates data
fragmentation as a possible way to protect privacy of data
associations and provide fragmentation as a complementary means for
protecting privacy: associations broken by fragmentation will be
visible only to users authorized (by knowing the proper key) to
join fragments. The book finally investigates the problem of
executing queries over possible data distributed at different
servers and which must be controlled to ensure sensitive
information and sensitive associations be visible only to parties
authorized for that. Case Studies are provided throughout the
book.
Privacy, data mining, data protection, data outsourcing,
electronic commerce, machine learning professionals and others
working in these related fields will find this book a valuable
asset, as well as primary associations such as ACM, IEEE and
Management Science. This book is also suitable for advanced level
students and researchers concentrating on computer science as a
secondary text or reference book.
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