PIWS: Private Intersection Weighted Sum Protocol for Privacy-Preserving Score-Based Voting With Perfect Ballot Secrecy

Abstract

This article proposes private intersection weighted sum (PIWS), a scalable, fair, and privacy-preserving intersection weighted sum protocol and applies it to voting scenarios. The PIWS protocol can privately calculate the intersection of identity index sets maintained by each participant and can privately calculate the weighted sum of the data associated with the identity indexes of the intersection set. After the execution of the protocol, both parties can only know the weighted sum, but not any additional information, such as any identity index or associated data of the other party. The PIWS protocol is very suitable for the privacy-preserving weighted voting scenarios and has three novel characteristics. First, it does not require as many semitrusted tally clerks as other protocols, which greatly reduces the deployment, communication, and calculation costs involved. It only requires the distributed deployment of voting servers and weight servers that are honest but curious. This is consistent with the deployment framework of the future big data application backgrounds. Second, perfect privacy protection and ballot secrecy are achieved. That is, the voting terminal or polling station provides encryption services for ballots immediately after each ballot is cast. All voting information is then expressed in ciphertext throughout the weighting and counting processes, until the final result of the weighted vote is passed to the voting server in the ciphertext. After decryption, the voting server only knows the results of the voting and it has no knowledge of the content or preference of the ballots, the privacy of the voters, or even the process of counting the votes. This design avoids the disclosure of voter privacy and ballot information, and the ciphertext form also prevents malicious users from cheating or tampering with voter or ballot information during the counting process. To better explain the security of our protocol, we present the provable security of the protocol under the honest-but-curious model and show the formal verification obtained using the Tamarin prover software. Third, our protocol not only achieves the function of an optional weighted voting protocol but also is relatively lightweight and efficient. The efficiency analysis results of the deployed voting system in terms of communication, storage, and calculation show that the protocol meets the requirements applicable to real-world applications. In summary, PIWS is superior to existing voting protocols in terms of function, security, and efficiency, and can be harmoniously applied to model updating of federated learning, consensus building of blockchain systems, or decision-making in artificial intelligence.