Preserving Privacy in Collaborative Systems with Secure Multi-Party Summation

In collaborative systems, ensuring the security and privacy of each party’s data is a concern when processing multi-party data. This paper proposes a new secure multi-party summation method based on cyclic groups. Utilizing the properties of cyclic groups, this method effectively counters the collusion attack in multi-party summation within collaborative systems, thereby enhancing the algorithm’s security. Compared with other similar algorithms, our method achieves a better balance between security and communication complexity. Furthermore, the security and performance of this method demonstrate flexibility, allowing for adaptation to meet specific requirements.