KEYWORDS: Seaborgium, Blockchain, Power grids, Information security, Design and modelling, Systems modeling, Power consumption, Data privacy, Astatine, Algorithm testing
With the continuous progress of new energy technology, new energy vehicles have gradually become the mainstream. More and more new Energy Vehicles (EV) are put into use, which not only brings challenges to the current Smart Grid (SG) system, but also brings brand new opportunities. Vehicle-to-grid (V2G) is a new technology, which not only realizes the power transmission from the grid to the Vehicle, but also realizes the auxiliary power transmission ability from the vehicle to the grid. But the privacy problem and the fairness of the transaction are still an urgent problem to be solved. Privacy issues can lead to the user's privacy being obtained and analyzed by malicious participants, resulting in the user's property being threatened. Fair exchange means that in the transaction process, if one party is malicious, the honest party has no loss or the loss is negligible, or if both parties are malicious or honest, there is no loss for either party. This paper proposes a Vehicle-to-grid privacy protection fair exchange system based on blockchain. Homomorphic commitment and hash chain are introduced to achieve privacy protection and fair exchange.
KEYWORDS: Education and training, Data modeling, Deep learning, Machine learning, Computer simulations, Data privacy, Systems modeling, Computer security
Federated learning enables multiple parties to jointly train a global model without sharing the original data, which has attracted much attention. Existing research work shows that even sharing local gradients will leak local data. What's worse, the server may deliberately tamper with the aggregation results, resulting in user privacy leakage or other attacks, so users need to verify the correctness of the calculation results returned by the server. In this paper, we design a verifiable privacy-preserving scheme where the server is honest and curious but has the additional ability to forge the aggregated results. The proposed scheme can guarantee the privacy gradient of honest users under the condition that no more than t users collude with the server. During the execution of the protocol, the user is allowed to drop out at any phase, and the aggregated results is kept secret from the server. In addition, each user can verify the correctness of the server’s calculation results, which is the ciphertext of the aggregated results.
KEYWORDS: Clouds, Matrices, Computer security, Internet of things, Receivers, Mobile devices, Cloud computing, Telecommunications, Systems modeling, Internet
Due to the storage and computing ability of cloud technology, many protocols are suitable for deployment on cloud servers. Private set intersection (PSI) is practical technology in data mining, similar document detection and so on. In some cloud-based IoT system and mobile devices have poor ability to calculate the intersection. In this scenario, we design a protocol make the part of the computing intersection execute on the cloud server, called Efficient-DPSI, which supports flexible parameters. Experimental results show that our Efficient-DPSI protocol is more efficient with existing related delegated PSI protocols.
Federated learning (FL) enables decentralized data sources like mobile phones to joint training a neural network model without sharing the original data. However, shared local gradients make the privacy of local data in FL vulnerable. The aggregation server also may return incorrect results to clients due to unexpected error or the deliberately attack. In this work, we explore how to design a non-interactive and publicly verifiable aggregation scheme. The existing verifiable schemes are under semi-honest adversary model, in which the server is honest-but-curious but with additional power to counterfeit the aggregation result. We propose a scheme under stronger security model against malicious servers. The proposed scheme guarantees that as long as the two servers are non-colluding, even a malicious server cannot obtain input privacy of client. The malicious server will be detected by honest clients when it tries to tamper the result.
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