Shared and secured data partitioning for privacy preserving of collaborative file transfer in multi path computational mining

Many techniques have been presented earlier to share the data online with privacy terms. But the downside is that the privacy preservation of data is unreliable when large set of data has been shared. The previous work describes only the data sharing process in a secure manner using session based secured multiparty combination and there is none other reliable approach yet to be defined for file sharing concepts through online in a secure manner. To present a file sharing approach online in a reliable manner, the proposal in this work presented collaborative file sharing (CFS) mechanism using Anchor technique by preserving the privacy of data. An Anchor technique (AT) will be used here for file partitioning schemes based on the location of the file. In addition to this, file sharing mechanism in a secured way will be handled using multiparty computation with association rule mining which allows the files to be shared for more than one user and reduces the adversary attack and the session overhead problem. The parties whoever involved in file sharing approach in CFS with AT can share their valid data without disclosing their privacy of data with each other. To evaluate the privacy performance of the proposed CFS with AT, the experimental evaluation is done with several user clients in terms of communication key round, number of participants and the size of the file for exchange. The outcome of the experiment clearly shows that the proposed collaborative file sharing (CFS with AT) mechanism provides better improvement in file sharing concepts contrast to an existing SMC for collaborative data.