Privacy-Preserving Data Aggregation with Public Verifiability Against Internal Adversaries

Large amounts of data are continuously generated by individuals, apps, or dedicated devices. These data can be aggregated to compute useful statistics from multiple sources using data aggregation protocols. However, oftentimes these data contain private information that must be protected from misuse. Privacy-preserving protocols can help to...

Secure Smart Contract-Based Data Aggregation using Homomorphic Encryption

Privacy and security are important topics in data aggregation, as companies are aggregating large amounts of sensitive data from their users. The aim of this paper is to improve the security and privacy of data aggregation using smart contracts, homomorphic encryption and post-quantum encryption methods on Hyperledger Fabric. After a literature...

Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption...