Secure Land Purchasing using Different Multi-Party Skyline Queries with Anonymous Information

A large number of available information causes data overload for users to choose the optimal options. In case of online land purchasing, it is challenging for a user to choose an optimal option from a vast number of lands with different areas and prices. Again, datasets of the seller (i. e. land owner) are sensitive since it is a business issue. Therefore, ensuring the privacy of the information, anonymity of the seller and their information etc. are crucial. This paper proposes a framework to find out the dominant (i. e. optimal) datasets using several skyline query algorithms namely block nested loop (BNL), bitmap, index, and nearest neighbour (NN). Besides, to ensure data privacy, separate decryption keys are allocated among multiple authorities, to ensure data anonymity, mixed network-based ElGamal cryptosystem with re-encryption and shuffling techniques are used, to ensure anonymity of the seller, an anonymous identity after registration phase is assigned. Lastly, The selection processes, privacy, result analyses etc. shows the efficiency of the framework and makes easier for land buyers to choose the optimal options. This paper is a preliminary report of the proposed system.