An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges by Andrei Tchernykh, Mikhail Babenko, Egor Shiriaev, Bernardo Pulido-Gaytan, Jorge M. Cortés-Mendoza, Arutyun Avetisyan, Alexander Yu Drozdov, Viktor Kuchukov

“…Fully Homomorphic Encryption (FHE) permits processing information in the form of ciphertexts without decryption. …”
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Obfuscation of Probabilistic Circuits and Applications by Ananth, Prabhanjan, Brakerski, Zvika, Segev, Gil, Vaikuntananthan, Vinod

“…In particular, we first give a general and natural methodology to achieve fully homomorphic encryption (FHE) from variants of pIO and of semantically secure encryption schemes. …”
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Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets by Hong, Matthew M.

“…To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals, and to further classify their degrees of relatedness, all without sharing any private data. …”
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Towards an interpreter for efficient encrypted computation by Devadas, Srinivas, Fletcher, Christopher Wardlaw, Van Dijk, Marten

“…Fully homomorphic encryption (FHE) techniques are capable of performing encrypted computation on Boolean circuits, i.e., the user specifies encrypted inputs to the program, and the server computes on the encrypted inputs. …”
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HCDA: Efficient Pairing-Free Homographic Key Management for Dynamic Cross-Domain Authentication in VANETs by Haowen Tan, Shichang Xuan, Ilyong Chung

“…Please note that RSUs are assumed to be semi-trustworthy entity in our design, where critical vehicular keying messages remain secrecy. Homomorphic encryption design is applied for all involved RSUs and vehicles. …”
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Bounded Surjective Quadratic Functions over Fnp for MPC-/ZK-/FHE-Friendly Symmetric Primitives by Lorenzo Grassi

“… Motivated by new applications such as secure Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero-Knowledge proofs (ZK), many MPC-, FHE- and ZK-friendly symmetric-key primitives that minimize the< number of multiplications over Fp for a large prime p have been recently proposed in the literature. …”
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Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification by Guoqiang Deng, Min Tang, Yuhao Zhang, Ying Huang, Xuefeng Duan

“…State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. …”
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Invertible Quadratic Non-Linear Layers for MPC-/FHE-/ZK-Friendly Schemes over Fnp by Lorenzo Grassi, Silvia Onofri, Marco Pedicini, Luca Sozzi

“… Motivated by new applications such as secure Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero-Knowledge proofs (ZK), many MPC-, FHE- and ZK-friendly symmetric-key primitives that minimize the number of multiplications over Fp for a large prime p have been recently proposed in the literature. …”
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Survey on blockchain privacy protection techniques in cryptography by Feng LIU, Jie YANG, Jiayin QI

“…In recent years, the issue of data privacy has attracted increased attention, and how to achieve effective privacy protection in blockchain is a new research hotspot.In view of the current research status and development trend of blockchain in privacy protection, the privacy protection methods of blockchain in transaction address,prophecy machine and smart contract were explained, and the privacy strategies of blockchain in the protection of basic elements were summarized.Based on high-level literature at home and abroad, two types of blockchain cryptographic protection methods and usage scenarios were analyzed, including special cryptographic primitives and post-quantum cryptography.The advantages and disadvantages of seven cryptographic techniques applicable to current blockchain privacy protection were also reviewed, including attribute-based encryption, special data signature, homomorphic encryption, secure multi-party computation, zero-knowledge proofs, and lattice ciphers.It was concluded that the privacy protection of blockchain applications cannot be achieved without cryptographic technology.Meanwhile, the blockchain privacy protection technologies were analyzed in terms of both basic element protection and cryptographic protection.It was concluded that it was difficult to effectively solve the privacy problem only from the application and contract layers of the blockchain, and various cryptographic technologies should be used to complement each other according to different needs and application scenarios.In addition, according to the current development status of blockchain privacy cryptography, the narrative was developed from blockchain basic element protection and cryptography-based protection.From the perspectives of both endogenous basic element security and exogenous cryptographic privacy security, basic element privacy protection should be studied first, followed by an in-depth analysis of cryptographic protection techniques for blockchain privacy.The strengths and weaknesses and the potential value of the privacy handling aspects of the corresponding safeguards should be measured in terms of the development of technology in conjunction with practical applications, while considering the timeliness of the technology.Finally, an outlook on the future direction of blockchain privacy protection technologies was provided, indicating the issues that need to be addressed in focus.…”
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A efficient and robust privacy-preserving framework for cross-device federated learning by Weidong Du, Min Li, Liqiang Wu, Yiliang Han, Tanping Zhou, Xiaoyuan Yang

“…Abstract 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. …”
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Secure k -ish Nearest Neighbors Classifier by Shaul, Hayim, Feldman, Dan, Rus, Daniela

“…In this work we present a classifier based on <jats:italic>k</jats:italic>NN, that is more efficient to implement with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make to consider <jats:italic>κ</jats:italic> nearest neighbors for <jats:italic>κ ≈k</jats:italic> with probability that increases as the statistical distance between Gaussian and the distribution of the distances from <jats:italic>q</jats:italic> to <jats:italic>S</jats:italic> decreases. …”
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Advancing SCRAM: Privacy-Centric Approaches in Cyber Risk Measurement by Magrefty, David S.

“…The framework, through the use of Multi-Party Computation (MPC) and Homomorphic Encryption (HE), guarantees each party that their participation in the computation is confidential and that the aggregated results will not be decrypted without their authorization [1]. …”
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Identifying and exploiting structures for reliable deep learning by Sanyal, A

“…Then we propose the use of a Fully Homomorphic Encryption [84] scheme which can be used with a Binary neural network [61], along with a set of algebraic and computational tricks, to satisfy all our conditions for EPAAS while being computationally efficient.…”

Designing and Implementing a Privacy Preserving Record Linkage Protocol by Tom Gee, Brendan Behan, Shannon Lefaivre, Mahmoud Azimaee, Moyez Dharsee, Khaled El Emam, Julie Yang, Anthony Vaccarino, Kenneth Evans, J. Charles Victor, Elizabeth Theriault

“…Results Brain-CODE Link allows a deterministic linkage between encrypted identifiers (OHIP numbers), without revealing participant identity. The same homomorphic encryption algorithm applied to identifiers upon entry to Brain-CODE, is applied to relevant identifiers within ICES data holdings. …”
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Algebraic Attack on FHE-Friendly Cipher HERA Using Multiple Collisions by Fukang Liu, Abul Kalam, Santanu Sarkar, Willi Meier

“… Fully homomorphic encryption (FHE) is an advanced cryptography technique to allow computations (i.e., addition and multiplication) over encrypted data. …”
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On Deniable Computation and Sublinear Graph Algorithms by Mossel, Saleet

“…We define and construct Deniable Fully Homomorphic Encryption based on the Learning With Errors (LWE) polynomial hardness assumption. …”
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Practical Cryptographically Private and Verifiable Computation through Hardware-Software Co-Design by Samardzic, Nikola

“…Fully Homomorphic Encryption (FHE) and Verifiable Computation (VC) enable offloading computation to untrusted servers with cryptographic privacy and integrity guarantees. …”
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Ascend : an architecture for performing secure computation on encrypted data by Fletcher, Christopher W. (Christopher Wardlaw)

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Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model by Jasim, Ahmed Abdulhadi, Idris, Mohd Yamani Idna, Azzuhri, Saaidal Razalli, Issa, Noor Riyadh, Noor, Noorzaily Mohamed, Kakarla, Jagadeesh, Amiri, Iraj Sadegh

“…Furthermore, the attacks are detected and prevented by utilizing secure node authentication, data fragmentation algorithms, fully homomorphic encryption, and access control model. The secure node authentication algorithm prevents attacks from accessing the network. …”

Structure vs. hardness through the obfuscation lens by Degwekar, Akshay (Akshay Dhananjai)

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