Abstract
Nowadays, Cloud computing becomes a looming computing prototype. Users can get a variety of services such as high computation power, storage, etc. Thus, applications of users can be more cost-effectively put on cloud by utilizing various commodity computers together. But, cloud computing faces some security concerns even if they provides many services. Some of such important concerns are data security and privacy of data. Some personal data like personal healthcare records and financial records contain sensitive information which can be analyzed and mined for public researches although these records offer important human assets. Data should be privacy preserved because malicious cloud users or untrusted cloud providers can get the data with less effort. To deal with these problems, privacy-aware set-valued data publishing on cloud for personal healthcare records has been proposed. An efficient privacy-aware system, named PHKEM (Personal Healthcare k-anonymity Encryption Model), for eliminating privacy breaches in publishing of personal healthcare data on cloud as well as data querying is designed. A data anonymization technique, named k-Anonymity with extended quasi-identifier partitioning (EQI-partitioning), interactive differential privacy, and AES encryption is applied to preserving personal healthcare records to prevent unauthorized access. Therefore, the security is efficiently enhanced with a natural and expressive fashion.
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Alexander, E., Sathyalakshmi (2017). Privacy-Aware Set-Valued Data Publishing on Cloud for Personal Healthcare Records. In: Dash, S., Vijayakumar, K., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-10-3174-8_29
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DOI: https://doi.org/10.1007/978-981-10-3174-8_29
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