Abstract
In this paper, we focus on Elliptic Curve Cryptography based approach for Secure Multiparty Computation (SMC) problem. Widespread proliferation of data and the growth of communication technologies have enabled collaborative computations among parties in distributed scenario. Preserving privacy of data owned by parties is crucial in such scenarios. Classical approach to SMC is to perform computation using Trusted Third Party (TTP). However, in practical scenario, TTPs are hard to achieve and it is imperative to eliminate TTP in SMC. In addition, existing solutions proposed for SMC use classical homomorphic encryption schemes such as RSA and Paillier. Due to the higher cost incurred by such cryptosystems, the resultant SMC protocols are not scalable. We propose Elliptic Curve Cryptography (ECC) based approach for SMC that is scalable in terms of computational and communication cost and avoids TTP. In literature, there do exist various ECC based homomorphic schemes and it is imperative to investigate and analyze these schemes in order to select the suitable for a given application. In this paper, we empirically analyze various ECC based homomorphic encryption schemes based on performance metrics such as computational cost and communication cost. We recommend an efficient algorithm amongst several selected ones, that offers security with lesser overheads and can be applied in any application demanding privacy.
Highlights
Our handhelds have become smaller; computers faster; disks larger; networks more efficient and we enjoy bandwidths like never before; everything grew exponentially
We focus on comparative evaluation of Elliptic Curve Cryptography (ECC) based homomorphic encryption schemes to implement secure multiparty addition
We evaluate ECC based encryption schemes for secure multiparty addition based on two metrics viz. the computational cost and the communication cost
Summary
Our handhelds have become smaller; computers faster; disks larger; networks more efficient and we enjoy bandwidths like never before; everything grew exponentially. All this sums up to a very favourable environment for data collection, transfer and storage. There is a need to protocol a device that performs joint computation on private data without revealing data to other parties. The multi-party computation problem was introduced by Yao [15] and extended by Goldreich, Micali and Wigderson [16]. The basic method they use is to represent the problem as combinatorial circuit. Various applications such as Privacy Preserving Data Mining [17,18], Private statistical information retrieval [19,20], Privacy Preserving Database access [21] have been proposed that demand Secure Multiparty Computation among parties
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