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@article{152498,
        author = {Dalton Fernandes  and Amey Sawant  and Sarvesh Vani and Aghan Lawande and Poojan Vaigankar and Prof. Valerie Menezes and Prof. Kedar Sawant},
        title = {COMPARISON OF DATA LEAKAGE DETECTION AND PREVENTION ALGORITHMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {633-638},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152498},
        abstract = {Data anonymization is process by which personal data is altered in such a way that a data subject can no longer be identified directly or indirectly and the process sometimes irreversible, either by the data controller alone or in collaboration with any other party. Data anonymization may by accident enable the transfer of information across a boundary, such as between two departments within an organization or between two agencies, while reducing the risk of inadvertent divulge, and in certain environments in a way that enables evaluation and analytical post-anonymization. In the context of medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information but it’s just enough to gain desired knowledge which will not affect privacy. The name, address, and postcode must be removed, together with any other information which, in cooperation with other data held by or disclosed to the recipient, could identify the patient. There has always been a risk that anonymized data may not stay anonymous for a long period of time. Pairing the anonymized dataset with other data, advanced techniques and raw power are some of the ways previously anonymous data sets have become de-anonymized. The data subjects are no longer anonymous.},
        keywords = {Data Leakage, Data Anonymization, Prevention Algorithms.},
        month = {},
        }