In this paper, we explore the concept of distributed average consensus in a decentralized network. The problem at hand is to enable each node in the network to obtain an accurate estimate of the average value of private data held by other nodes without compromising privacy. The authors examine two approaches to address this challenge: centralized and decentralized solutions.
Centralized Solutions
In a centralized approach, a trusted third party coordinates the entire process, which raises significant privacy concerns. The authors argue that fully distributed/decentralized solutions are more appropriate for ensuring privacy since they do not require any centralized coordination.
Decentralized Solutions
The computational complexity of decentralized solutions depends heavily on the security model used. The authors focus on information-theoretical security based algorithms, which are computationally less complex than computational security based approaches. They also highlight that most existing work in this area concentrates on privacy and accuracy while neglecting communication costs.
Privacy-Preserving Distributed Average Consensus
To address the privacy concerns, the authors propose a privacy-preserving distributed average consensus algorithm that ensures accurate estimation without violating privacy. They consider five essential aspects when designing such algorithms: centralized vs. decentralized coordination, computational complexity, communication costs, and the achieved privacy level and output accuracy. The proposed approach achieves similar performances as a secure multi-party computation (SMPC) approach while ensuring better privacy protection by adding noise to the data.
Key Takeaways
- Distributed average consensus is crucial in decentralized networks where nodes need to estimate the average value of private data without compromising privacy.
- Fully decentralized solutions are more suitable than centralized approaches for ensuring privacy.
- The computational complexity of decentralized solutions depends on the security model used.
- Most existing work focuses on privacy and accuracy while neglecting communication costs.
- A privacy-preserving distributed average consensus algorithm can achieve accurate estimation without violating privacy by considering essential aspects like centralized vs. decentralized coordination, computational complexity, communication costs, and achieved privacy level and output accuracy.
