Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus

Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches...

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...

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...

Privacy-preserving of system model with perturbed state trajectories using differential privacy: With application to a supply chain network

Releasing state samples generated by a dynamical system model, for data aggregation purposes, can allow an adversary to perform reverse engineering and estimate sensitive model parameters. Upon identification of the system model, the adversary may even use it for predicting sensitive data in the future. Hence, preserving a confidential dynamical...

Extreme Chaos: Flexible and Efficient All-to-All Data Aggregation for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are networks of sensor devices that perform environmental monitoring applications. Data aggregation is a vital building block for WSNs that enables rapid gather the information held by the network nodes. A continuous study of different approaches to retrieving aggregates is being carried out by the researchers of...