Абстрактный

Privacy Preserved Association Rule Mining For Attack Detection and Prevention

V.Ragunath, C.R.Dhivya

In this project, a company to protect the corporate privacy, the data owner transforms its data and shifts it to the server and recovers the true patterns from the extracted patterns received from the server. The client owner encrypts its data using an (E/D) module. Our encryption scheme has the property that the returned supports are not true supports. The encrypt/decrypt module recovers the true identity of the returned patterns as well their true supports. It is trivial to show that if the data are encrypted using 1–1 substitution ciphers (without using fake transactions), many ciphers and hence the transactions and patterns can be broken by the server with a high probability by launching the frequency-based attack. The data owner recover true pattern from the E/D module by using incremental maintenance. The privacy guarantees of our method in case of known-plaintext attacks, chosen-plaintext attacks and chosen-cipher text attacks. Finally which one has access the data by unauthorized permission that one can be detected and removed from particular group.