Affiliations: [a] Department of Computer Science and Engineering, Srinidhi Institute of Science and Technology, Hyderabad, India
| [b] Department of Computer Science and Engineering, QIS College of Engineering and Technology, Ongole, India
| [c] Department of Computer Science and Engineering, VIT University, Vellore, India
Correspondence:
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Corresponding author. Dr. M.N. Faruk, Department of Computer Science and Engineering, QIS College of Engineering and Technology, Ongole, Andhra Pradesh, India. Mobile: +91 9943220111; Tel.: +91 8592 284524; Fax: +91 8592 281023; E-mail: [email protected].
Abstract: Cloud computing plays a predominant role in storage technologies. It enables the tenant user to deploy their infrastructure without any investment. Cloud storage offers flexibility with storage and sharing facilities using the Internet platform. Storing sensitive information such as clinical data requires high privacy preservation and is associated with serious concern over data privacy on the cloud platform. Privacy preservation becomes the most adherent issue when a large volume of data is stored in public clouds. Subtree anonymization using the bottom–up generalization (BUG) and top–down specialization (TDS) approaches has been widely adopted for anonymizing data sets. This ensures individual data privacy; however, it causes potential violations when the new update is received, and it suffers from valuing the k-anonymity parameter. In this proposed model, a pseudo-identity was anticipated to accomplish privacy preservation with maximum data utility on incremental data sets. Initially, the Data Set (DS) was partitioned in the preprocessing stage; subsequently, the processed data sets were clustered into groups. The genetic model was used for indexing and updating incremental data sets. This was consistent with repeatedly modified data sets. In the evaluation process, an incremental and distributed DS was deployed, and our model exhibited efficient and optimal performance for privacy preservation in comparison with existing models.
Keywords: Subtree anonymization, bottom–up generalization, top–down specialization, k-anonymity, data set partitioning
