Abstract
Privacy preservation is widely talked in recent years, which prevents the disclosure of sensitive information during the knowledge discovery. There are many applications of distributed scenario which includes retail shops, where the stream of digital data is collected from time to time. The collaborating parties are generally interested in finding global patterns for their mutual benefits. There are few proposals which address these issues, but in the existing methods, global pattern computation is carried out by one of the source itself and uses one offset to perturb the personal data which fails in many situations such as all the patterns are not initiated at the initial participating party. Our novel approach addresses these problems for retail shops in strategic way by considering the different offsets to perturb the sensitive information and trusted third party to ensure global pattern computation.
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© 2011 Springer-Verlag Berlin Heidelberg
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B.N., K., Toshniwal, D. (2011). Privacy Preservation of Stream Data Patterns Using Offset and Trusted Third Party Computation in Retail-Shop Market Basket Analysis. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advanced Computing. CCSIT 2011. Communications in Computer and Information Science, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17881-8_34
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DOI: https://doi.org/10.1007/978-3-642-17881-8_34
Publisher Name: Springer, Berlin, Heidelberg
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