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With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) gradient selection algorithm is used to filter the gradient of encryption and upload efficiently, which reduces the computing overhead of 51.78% and the traffic of 64.87% on the premise of ensuring the accuracy of the model, which makes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals. [ABSTRACT FROM AUTHOR]

With the surge in cloud storage popularity, more individuals are choosing to store large amounts of data on remote cloud service providers (CSPs) to save local storage resources. However, users' primary worries revolve around maintaining data integrity and authenticity. Consequently, several cloud auditing methods have emerged to address these concerns. Many of these approaches rely on traditional public-key cryptography systems or are grounded in identity-based cryptography systems or certificateless cryptography systems. However, they are vulnerable to the increased costs linked with certificate management, key escrow, or the significant expenses of establishing a secure channel, respectively. To counter these limitations, Li et al. introduced a certificate-based cloud auditing protocol (LZ22), notable for its minimal tag generation overhead. Nonetheless, this protocol exhibits certain security vulnerabilities. In this paper, we devise a counterfeiting technique that allows the CSP to produce a counterfeit data block with an identical tag to the original one. Our counterfeiting method boasts a 100% success rate ∀ data block and operates with exceptional efficiency. The counterfeiting process for a single block of 10 kB, 50 kB, and 100 kB takes a maximum of 0.08 s, 0.51 s, and 1.04 s, respectively. By substituting the exponential component of homomorphic verifiable tags (HVTs) with non-public random elements, we formulate a secure certificate-based cloud auditing protocol. In comparison to the LZ22 protocol, the average tag generation overhead of our proposed protocol is reduced by 6.80%, 13.78%, and 8.66% for data sizes of 10 kB, 50 kB, and 100 kB, respectively. However, the auditing overhead of our proposed protocol shows an increase. The average overhead rises by 3.05%, 0.17%, and 0.45% over the LZ22 protocol's overhead for data sizes of 10 kB, 50 kB, and 100 kB, correspondingly. [ABSTRACT FROM AUTHOR]

To ensure no private information is leaked in the aggregation phase in federated learning (FL), many frameworks use homomorphic encryption (HE) to mask local model updates. However, the heavy overheads of these frameworks make them unsuitable for cross-device FL, where the clients are a huge number of mobile and edge devices with limited computing resources. Even worse, some of them also fail to manage the dynamic changes of clients. To overcome these shortcomings, we propose a threshold multi-key HE scheme tMK-CKKS and design an efficient and robust privacy-preserving FL framework. Robustness means that our framework allows clients to join in or drop out during the training process. Besides, because our tMK-CKKS scheme can pack multiple messages in a single ciphertext, our framework significantly reduces the computation and communication overhead. Moreover, the threshold mechanism in tMK-CKKS ensures that our framework can resist collusion attacks between the server and no more than t (threshold value) curious internal clients. Finally, we implement our framework in FedML and conduct extensive experiments to evaluate our framework. Utility evaluations on 6 benchmark datasets show that our framework can protect privacy without sacrificing the model accuracy. Efficiency evaluations on 4 typical deep learning models demonstrate that: our framework can speed up the computation by at least 1.21 × over xMK-CKKS-based framework, 15.84 × over Batchcrypt-based framework, and 20.30 × over CRT-Paillier-based framework. Our framework can reduce the communication burden by at least 8.61 MB over Batchcrypt-based framework, 35.36 MB over xMK-CKKS-based framework and 42.58 MB over CRT-Paillier-based framework. The advantages in both computation and communication expand with the size of deep learning models. [ABSTRACT FROM AUTHOR]

Multi-key fully homomorphic encryption (MFHE) supports arbitrary meaningful computations on encrypted data under different public keys even without access to the secret key, which is well tailored for the secure multiparty computation scenarios. Based on the Gentry–Sahai–Waters scheme (a single-key FHE in Crypto 2013) with the underlying learning with errors problem, MW16 scheme (Eurocrypt 2016) utilizes the method of 'linear combination procedure' (LCP) as a subroutine to construct the auxiliary information for the expanded ciphertexts of MFHE scheme. However, every party shares a common random string (CRS) to be distributed by a trusted setup, which is unpractical. Meanwhile, the noise in the auxiliary information is too much compared with the one in fresh ciphertexts. In this paper, we propose a modified MFHE scheme in the plain model, i.e. without CRS, to enhance the practicability of MFHE. Specifically, every involved party generates his own public key independent on a CRS. Then a potential improvement on the LCP is developed to provide auxiliary information, which largely reduces the noise and leads to a smaller modulus for our MFHE. Furthermore, the feasibility of our proposal is also proved by theoretical performance comparisons. [ABSTRACT FROM AUTHOR]

Smart grids are a combination of traditional power system engineering as well as information and communications technology. Smart grid terminals provide convenient services to users by aggregating their data in real time. However, terminals can derive user privacy information from real-time data on smart devices. Therefore, security data aggregation has been widely studied in the field of smart grid. Most existing schemes are one-dimensional data aggregation or rely on a trusted third party. In reality, multidimensional data (such as a user's electricity consumption or user's main usage time, etc.) makes sense for terminals to flexibly adjust supply and demand strategies. In this paper, we propose an efficient and secure multidimensional data aggregation scheme that supports batch validation without a trusted third party. Firstly, we apply the Chinese remainder theorem to encode the user's multidimensional data and realize the independence of each dimension in terminal decryption. Secondly, we adopt a secure key negotiation protocol that does not require a trusted third party. Finally, based on paillier homomorphic encryption and bilinear pairing, we construct an encryption scheme that can reuse the key and blind factor and support batch verification. The analysis results show that our scheme is secure for users' privacy protection. Experimental results show that, compared with existing 1 dimensional aggregation schemes, our scheme has almost no growth in computational overhead for terminal decryption. [ABSTRACT FROM AUTHOR]

Multi‐key fully homomorphic encryption (MKFHE) allows computations on ciphertexts encrypted by different users, which can be applied to implement secure multi‐party computing (MPC). The current NTRU‐based MKFHE has the following two drawbacks: One is that the relinearisation process during homomorphic evaluation is so complicated that the corresponding computation time is costly. The other is that a class of subfield attacks are proposed and affects the security of NTRU schemes over power‐of‐2 cyclotomic rings for large moduli q, especially for the NTRU‐based fully homomorphic encryption (FHE) schemes. In this work, an efficient MKFHE scheme is proposed over prime cyclotomic rings with fewer relinearisations, which seems a good choice because of its potential to resist a subfield attack. More specifically, the time of the relinearisation process is reduced by half in homomorphic evaluations by separating the homomorphic multiplication and the relinearisation process (implementing two homomorphic multiplication operations together before relinearisation), while in current NTRU‐type MKFHE schemes, these two processes are usually performed together. The error bound of the basic function components is re‐analysed over prime cyclotomic rings in the average case, which can be used in the error analysis of our scheme. We construct an efficient NTRU‐based single‐key FHE scheme and an efficient MKFHE scheme over prime cyclotomic rings through relinearisation and modulus‐switching techniques. The MKFHE scheme proposed has the on‐the‐fly property and has a tight ciphertext size compared with the GSW‐type and BGV‐type MKFHE schemes. An experiment shows that the homomorphic evaluation of the optimised single‐key FHE scheme proposed is 1.9 times faster than an efficient NTRU‐type MKFHE DHS16 proposed at DCC 2016. [ABSTRACT FROM AUTHOR]

Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user's location privacy in LBSs, without considering the user group's location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group's three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend's distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user's location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group. [ABSTRACT FROM AUTHOR]

The security of multimedia content and personal privacy for big data has triggered widespread concern in the society. Fully homomorphic encryption (FHE), which can homomorphically compute arbitrary functions on the encrypted data without knowing the secret key, is valuable in protecting user's data security. However, most of the FHE schemes only take single-bit of ciphertext as the input, which makes the evaluation process complicated. In EUROCRYPT'2015, Ducas and Micciancio proposed an FHE scheme FHEW with the plaintext space , and gave an assumption of extending the plaintext space to . In this paper, we optimize the decryption algorithm of FHEW in bootstrapping, and propose an FHE scheme with large plaintext space . Firstly, we optimize the rounding function of the decryption algorithm in FHEW to the msdExtract algorithm, which can homomorphically extract the most significant digit of the plaintext. Secondly, we design the msdExtract algorithm by employing the homomorphic accumulator, and present the process of general bootstrapping. Finally, based on the msdExtract algorithm, we extend the plaintext space of our scheme to , comparing to in FHEW. The security of our scheme is based on the basic LWE scheme and FHEW. What's more, our scheme can perform the evaluation more conveniently with large plaintext space, and can be applied to more scenarios. [ABSTRACT FROM AUTHOR]