BibTeX:

@article{IJIRSTV6I6015,
     title={A Study on k-anonymity, l-diversity, and t-closeness Techniques of Privacy Preservation Data Publishing},
     author={Twinkle Patel and Dr Kiran Amin},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={6},
     number={6},
     pages={19--24},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV6I6015.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Most organizations are dealing with massive amounts of information collection and are stored in large databases. Personal health record (PHR) is an emerging patient-centered model of exchange of health information, often outsourced for third-party storage, such as cloud providers. There have been wide-ranging issues regarding privacy, however, as personal health data may be exposed to those third party servers and unauthorized parties. This work aims to highlight three of the popular strategies for clinical anonymization, namely k-anonymity, l-diversity, and t-closeness. There is also a summary of the benefits and weaknesses of these strategies. Extensive analytical and experimental findings are presented showing our proposed schemes security, scalability and performance.

health records; data anonymization; k-anonymity; l-diversity; t-closeness