Public-Key Cryptosystems Based on Composite Degree Residuosity Classes

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A Public-Key Cryptosystem Based On Algebraic Coding Theory

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Fully homomorphic encryption using ideal lattices

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SecureML: A System for Scalable Privacy-Preserving Machine Learning

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Evaluating 2-DNF Formulas on Ciphertexts

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Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

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Efficient Fully Homomorphic Encryption from (Standard) LWE

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

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A Survey on Homomorphic Encryption Schemes: Theory and Implementation

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Efficient Private Matching and Set Intersection

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CryptDB

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Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages

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Privacy Preserving Data Mining

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Federated learning on non-IID data: A survey

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A Generalisation, a Simplification and Some Applications of Paillier's Probabilistic Public-Key System

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A Hybrid Approach to Privacy-Preserving Federated Learning

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Can homomorphic encryption be practical?

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Security and Privacy on Blockchain

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Design and Analysis of Practical Public-Key Encryption Schemes Secure against Adaptive Chosen Ciphertext Attack

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ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation

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Universal Hash Proofs and a Paradigm for Adaptive Chosen Ciphertext Secure Public-Key Encryption

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A survey on privacy protection in blockchain system

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EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications

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Quadratic Span Programs and Succinct NIZKs without PCPs

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Oblivious Neural Network Predictions via MiniONN Transformations

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Machine Learning Classification over Encrypted Data

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Charm: a framework for rapidly prototyping cryptosystems

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SecureBoost: A Lossless Federated Learning Framework

Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2021) · IEEE Intelligent Systems · doi:10.1109/mis.2021.3082561 · Cited by 669

Security and Privacy for Cloud-Based IoT: Challenges

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Privacy-Preserving Set Operations

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Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

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A secure federated transfer learning framework

Liu, Y., Kang, Y., Xing, C., Chen, T., & Yang, Q. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988525 · Cited by 645

Lossy Trapdoor Functions and Their Applications

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A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

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Privacy-preserving k -means clustering over vertically partitioned data

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DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems

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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610

Privacy-Preserving Face Recognition

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Multiparty Computation from Threshold Homomorphic Encryption

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Secure Information Aggregation for Smart Grids Using Homomorphic Encryption

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Secure Multiparty Computation for Privacy-Preserving Data Mining

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Privacy and Robustness in Federated Learning: Attacks and Defenses

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Data Security and Privacy Protection for Cloud Storage: A Survey

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Practical Verifiable Encryption and Decryption of Discrete Logarithms

Camenisch, J. L., & Shoup, V. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45146-4_8 · Cited by 543

A Decade of Lattice Cryptography

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Data security and privacy-preserving in edge computing paradigm: survey and open issues

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Homomorphic Signature Schemes

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Gazelle: A Low Latency Framework for Secure Neural Network InferencePreprint

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A Survey of Homomorphic Encryption for Nonspecialists

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A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

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Secure Federated Matrix Factorization

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Fully Homomorphic Encryption over the Integers with Shorter Public Keys

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Secure Single-Server Aggregation with (Poly)Logarithmic Overhead

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Routes for breaching and protecting genetic privacy

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Privacy‐preserving federated learning based on multi‐key homomorphic encryption

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Privacy-Preserving Ridge Regression on Hundreds of Millions of Records

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Coercion-resistant electronic elections

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Keyword Search and Oblivious Pseudorandom Functions

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Supply Chain Finance Innovation Using Blockchain

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Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

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Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

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Anatomy of Threats to the Internet of Things

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Practical Private Set Intersection Protocols with Linear Complexity

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Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

Li, Y., Wei, X., Li, Y., Dong, Z. Y., & Shahidehpour, M. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3204796 · Cited by 414

Civitas: Toward a Secure Voting System

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Securing smart grid: cyber attacks, countermeasures, and challenges

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Big data privacy: a technological perspective and review

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Priced Oblivious Transfer: How to Sell Digital Goods

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Federated Learning

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Secure k-nearest neighbor query over encrypted data in outsourced environments

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A New Randomness Extraction Paradigm for Hybrid Encryption

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Verifiable Delegation of Computation over Large Datasets

Benabbas, S., Gennaro, R., & Vahlis, Y. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22792-9_7 · Cited by 381

Privacy-Friendly Energy-Metering via Homomorphic Encryption

Garcia, F. D., & Jacobs, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22444-7_15 · Cited by 377

From distributed machine learning to federated learning: a survey

Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01664-x · Cited by 397

On Private Scalar Product Computation for Privacy-Preserving Data Mining

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Towards secure mobile cloud computing: a survey

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Efficient Privacy-Preserving Face Recognition

Sadeghi, A., Schneider, T. H., & Wehrenberg, I. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14423-3_16 · Cited by 366

Processing analytical queries over encrypted data

Tu, S., Kaashoek, M. F., Madden, S., & Zeldovich, N. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2535573.2488336 · Cited by 363

A Survey on Big Data Market: Pricing, Trading and Protection

Liang, F., Yu, W., An, D., Yang, Q., Fu, X., & Zhao, W. (2018) · IEEE Access · doi:10.1109/access.2018.2806881 · Cited by 361

Structure-Preserving Signatures and Commitments to Group Elements

Abe, M., Fuchsbauer, G., Groth, J., Haralambiev, K., & Ohkubo, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14623-7_12 · Cited by 357

A Survey of Provably Secure Searchable Encryption

Bösch, C., Hartel, P., Jonker, W., & Peter, A. (2014) · ACM Computing Surveys · doi:10.1145/2636328 · Cited by 357

Semi-homomorphic Encryption and Multiparty Computation

Bendlin, R., Damgård, I., Orlandi, C., & Zakarias, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20465-4_11 · Cited by 356

TASTY

Henecka, W., ögl, S. K., Sadeghi, A., Schneider, T. H., & Wehrenberg, I. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866358 · Cited by 356

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

Luong, N. C., Hoang, D. T., Wang, P., Niyato, D. T., Kim, D. I., & Han, Z. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2582841 · Cited by 364

On the Exact Security of Full Domain Hash

Coron, J.-S. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44598-6_14 · Cited by 354

Secure and Privacy-Preserving Consensus

Ruan, M., Gao, H. B., & Wang, Y. (2019) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2019.2890887 · Cited by 352

Privacy-Enhanced Federated Learning Against Poisoning Adversaries

Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3108434 · Cited by 351

Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT

Jia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., & Liang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3085960 · Cited by 358

Encrypted signal processing for privacy protection: Conveying the utility of homomorphic encryption and multiparty computation

Lagendijk, R. L., & Barni, M. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2219653 · Cited by 334

Authentication Protocols for Internet of Things: A Comprehensive Survey

Ferrag, M. A., Maglaras, L. A., Janicke, H., Jiang, J., & Shu, L. (2017) · Security and Communication Networks · doi:10.1155/2017/6562953 · Cited by 331

Sharing Decryption in the Context of Voting or Lotteries

Fouque, P.-A., Poupard, G., & Stern, J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45472-1_7 · Cited by 326

Toward efficient and privacy-preserving computing in big data era

Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. (2014) · IEEE Network · doi:10.1109/mnet.2014.6863131 · Cited by 325

Survey on Fully Homomorphic Encryption, Theory, and Applications

Marcolla, C., Sucasas, V., Manzano, M., Bassoli, R., Fitzek, F. H. P., & Aaraj, N. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3205665 · Cited by 323

Security and Privacy in Device-to-Device (D2D) Communication: A Review

Haus, M., Waqas, M. M., Ding, A. Y., Li, Y., Tarkoma, S., & Ott, J. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2649687 · Cited by 325

Secure and practical outsourcing of linear programming in cloud computing

Wang, C., Ren, K., & Wang, J. S. (2011) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2011.5935305 · Cited by 317

Robustness, Security and Privacy in Location-Based Services for Future IoT: A Survey

Chen, L., Thombre, S., Jarvinen, K., Lohan, E. S., Alen-Savikko, A., Leppakoski, H., Bhuiyan, M. Z. H., Bu-Pasha, S., Ferrara, G. N., Honkala, S., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2695525 · Cited by 313

Fully Secure Functional Encryption for Inner Products, from Standard Assumptions

Agrawal, S., Libert, B., & Stehlé, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53015-3_12 · Cited by 312

Smart Meter Data Privacy: A Survey

Asghar, M. R., Dán, G., Miorandi, D., & Imrich Chlamtac, A. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2720195 · Cited by 321

Secure Hybrid Encryption from Weakened Key Encapsulation

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Spatial crowdsourcing: a survey

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Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

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A new public key cryptosystem based on higher residues

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Efficient Public-Key Cryptosystems Provably Secure Against Active Adversaries

Paillier, P., Pointcheval, D., International, G. C., & Cryptography Department (1999)

Paillier Algorithm

Koç, Ç. K., Özdemir, F., & Özger, Z. Ö. (2021) · Springer eBooks · doi:10.1007/978-3-030-87629-6_9 · Cited by 1

A new public-key cryptosystem as secure as factoring

Okamoto, T., & Uchiyama, S. (1998) · Lecture notes in computer science · doi:10.1007/bfb0054135 · Cited by 674

Universal Hash Proofs and a Paradigm for Adaptive Chosen Ciphertext Secure Public-Key Encryption

Cramer, R., & Shoup, V. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46035-7_4 · Cited by 841