Privacy Enhanced Aggregation News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key ....

Privacy Enhanced Aggregation , Federated Learning , Car Evaluation , Computational Modeling , Data Aggregation , Data Models , Data Privacy , Federated Learning , Homomorphic Encryption , Obile Crowdsensing , Task Analysis ,