Future Privacy Of Data Analysis

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Future Privacy of Information Technologies to Protect Personal Data In a modern life, from the minute a person is born, a digital record is created. From that point on, the individual’s behavior is regularly tracked and information are collected about the typical parts of the person life such as when government collect data about our health, education and income, we hope that the data are used in good way. However, we always have concerned about our privacy. Liina Kamm did her research on the Information Security Research Institute of Cybernetica AS. Kamm worked with development team of the secure data analysis system Sharemind to develop she developed a convenient privacy-preserving data analysis tool Rmind to help on the future privacy …show more content…

Second, the Data users have an interest in gathering data to learn the statistical possessions of the qualities or notice designs from this date. Also, SHAREMIND is designed to be organized as a disseminated secure calculation benefit that can be used for outsourcing data storage and calculations. The benefits of SHAREMIND are the developer labels data as public or private in the database and in application code, Sharemind routinely applies cryptographic safety for private data throughout the analysis process step, and Private data cannot be made public without using special functions that need the compromise of some servers before publication data. Different type of privacy There are four type of privacy levels. they are record-level, source-level, output-level and cryptographic privacy. First, Record-level privacy is most important in statistical surveys and setups where databases are published. Second, source-level privacy is very like record-level privacy, but the difference is privacy does not easily report the issue of privacy leaks as a result of a large number of frequent requests. Third, Output-level privacy looks directly at the result the data analysis more specifically in how much the outputs of a data removal process leak material about its inputs. This is because breaking output-level privacy means that we learned something about the private inputs that we should not have learned. Forth, Cryptographic privacy guarantees that only the last

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