Privacy-Preserving Average Consensus via State Decomposition
نویسندگان
چکیده
منابع مشابه
Privacy-preserving Average Consensus: Privacy Analysis and Optimal Algorithm Design
The goal of the privacy-preserving average consensus (PPAC) is to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial value. This goal is achieved by an existing PPAC algorithm by adding and subtracting variance decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of t...
متن کاملSecure and Privacy-Preserving Consensus
Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which ...
متن کاملPrivacy-Preserving Discovery of Consensus Signatures
Network intrusion detection systems (NIDSs) play a key role in defending modern network computing environments against attacks. NIDS are most typically deployed as perimeter traffic monitors, evaluating packet flows and content against some form of known intrusion heuristics, often referred to as intrusion signatures. One key weaknesses with NIDS is their inability to react to “new threats” (i....
متن کاملDynamic Average Consensus under Limited Control Authority and Privacy Requirements
This paper introduces a novel continuous-time dynamic average consensus algorithm for networks whose interaction is described by a strongly connected and weight-balanced directed graph. The proposed distributed algorithm allows agents to track the average of their dynamic inputs with some steady-state error whose size can be controlled using a design parameter. This steady-state error vanishes ...
متن کاملPrivacy-Preserving Collaborative Web Services QoS Prediction via Differential Privacy
Collaborative Web services QoS prediction has become an important tool for the generation of accurate personalized QoS. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framewor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 2019
ISSN: 0018-9286,1558-2523,2334-3303
DOI: 10.1109/tac.2019.2902731