Multikey homomorphic encryption(MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers(e.g., cloud computing and artificial intel...Multikey homomorphic encryption(MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers(e.g., cloud computing and artificial intelligence) to protect user privacy. CDKS19 is the current most efficient MKHE scheme, and its relinearization process consumes most of the time of homomorphic evaluation. In this study, an optimized relinearization algorithm of CDKS19 is proposed. This algorithm reorganizes the evaluation key during the key generation process, decreases the complexity of relinearization, and reduces the error growth rate during homomorphic evaluation. First, we reduce the scale of the evaluation key by increasing its modulus instead of using a gadget vector to decompose the user’s public key and extend the ciphertext of homomorphic multiplication. Second, we use rescaling technology to optimize the relinearization algorithm;thus, the error bound of the ciphertext is reduced, and the homomorphic operation efficiency is improved. Lastly, the average-case error estimation on the variances of polynomial coefficients and the upper bound of the canonical embedding map are provided. Results show that our scheme reduces the scale of the evaluation key, the error variance, and the computational cost of the relinearization process. Our scheme can effectively perform the homomorphic multiplication of ciphertexts.展开更多
As a distributed machine learning architecture,Federated Learning(FL)can train a global model by exchanging users’model parameters without their local data.However,with the evolution of eavesdropping techniques,attac...As a distributed machine learning architecture,Federated Learning(FL)can train a global model by exchanging users’model parameters without their local data.However,with the evolution of eavesdropping techniques,attackers can infer information related to users’local data with the intercepted model parameters,resulting in privacy leakage and hindering the application of FL in smart factories.To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations,in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption(FHE),called MFHE-PPFL.Specifically,to reduce communication costs caused by deploying the FHE algorithm,we propose a self-adaptive threshold-based model parameter compression(SATMPC)method.It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server.Moreover,to protect model parameter privacy during transmission,we develop a secret sharing-based multi-key RNS-CKKS(SSMR)method that encrypts the device’s uploaded parameter increments and supports decryption in device dropout scenarios.Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.展开更多
基金supported by the National Key R&D Program of China (No. 2017YFB0802000)Innovative Research Team in Engineering University of PAP (No. KYTD201805)+2 种基金National Natural Science Foundation of China (No. 62172436)Natural Science Basic Research Plan in Shaanxi Province of China (No. 2020JQ492)Fundamental Research Project of Engineering University of PAP (Nos. WJY201910, WJY201914, and WJY201912)。
文摘Multikey homomorphic encryption(MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers(e.g., cloud computing and artificial intelligence) to protect user privacy. CDKS19 is the current most efficient MKHE scheme, and its relinearization process consumes most of the time of homomorphic evaluation. In this study, an optimized relinearization algorithm of CDKS19 is proposed. This algorithm reorganizes the evaluation key during the key generation process, decreases the complexity of relinearization, and reduces the error growth rate during homomorphic evaluation. First, we reduce the scale of the evaluation key by increasing its modulus instead of using a gadget vector to decompose the user’s public key and extend the ciphertext of homomorphic multiplication. Second, we use rescaling technology to optimize the relinearization algorithm;thus, the error bound of the ciphertext is reduced, and the homomorphic operation efficiency is improved. Lastly, the average-case error estimation on the variances of polynomial coefficients and the upper bound of the canonical embedding map are provided. Results show that our scheme reduces the scale of the evaluation key, the error variance, and the computational cost of the relinearization process. Our scheme can effectively perform the homomorphic multiplication of ciphertexts.
基金supported by the National Natural Science Foundation of China under Grant 62171113。
文摘As a distributed machine learning architecture,Federated Learning(FL)can train a global model by exchanging users’model parameters without their local data.However,with the evolution of eavesdropping techniques,attackers can infer information related to users’local data with the intercepted model parameters,resulting in privacy leakage and hindering the application of FL in smart factories.To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations,in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption(FHE),called MFHE-PPFL.Specifically,to reduce communication costs caused by deploying the FHE algorithm,we propose a self-adaptive threshold-based model parameter compression(SATMPC)method.It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server.Moreover,to protect model parameter privacy during transmission,we develop a secret sharing-based multi-key RNS-CKKS(SSMR)method that encrypts the device’s uploaded parameter increments and supports decryption in device dropout scenarios.Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.
