Privacy-Preserving Technologies: Homomorphic Encryption and Secure Multi-Party Computation

Abstract

With the increasing concerns over data privacy in cloud computing, healthcare, finance, and IoT, privacy-preserving technologies have gained significant attention. Among these, homomorphic encryption (HE) and secure multi-party computation (SMPC) stand out as powerful cryptographic techniques that enable computations on encrypted data without exposing sensitive information. This paper explores the principles, advancements, and real-world applications of HE and SMPC, comparing their strengths and limitations. We also discuss challenges in scalability, performance, and adoption, along with emerging trends in privacy-preserving computation.

Introduction

Overview

Traditional encryption protects data at rest and in transit but cannot perform computations on encrypted data. HE and SMPC enable secure computation without decrypting data, addressing privacy concerns in modern applications.

Homomorphic Encryption (HE)

• Concept: Allows arithmetic operations on ciphertexts, producing encrypted results equivalent to operations on plaintext.

• Types:

  • Partially Homomorphic Encryption (PHE): Supports one operation (addition or multiplication).

  • Somewhat Homomorphic Encryption (SHE): Supports limited operations before noise buildup.

  • Fully Homomorphic Encryption (FHE): Supports unlimited operations; pioneered by Gentry’s 2009 lattice-based scheme.

• Key Algorithms: Gentry’s scheme, BGV, BFV, CKKS; libraries include Microsoft SEAL, PALISADE, HElib.

• Applications: Secure cloud computing, private medical data analysis, encrypted financial computations.

• Challenges: High computational cost (FHE can be 1000x slower than plaintext), complex key management.

Secure Multi-Party Computation (SMPC)

• Concept: Multiple parties compute a function on private inputs without revealing them.

• Core Techniques:

  • Garbled Circuits: Two-party secure computation.

  • Secret Sharing (Shamir’s, BGW): Multi-party computation by splitting secrets into shares.

  • Oblivious Transfer: Secure data retrieval where the sender doesn’t know what data the receiver obtains.

  • Hybrids: Combining HE (for storage) and SMPC (for computation).

• Adversary Models:

  • Semi-Honest: Parties follow protocol but try to learn extra info.

  • Malicious: Parties may deviate; mitigated by zero-knowledge proofs and commitment schemes.

• Applications: Privacy-preserving machine learning, secure auctions, financial fraud detection, healthcare data sharing, blockchain/DeFi solutions.

• Advantages: Faster than FHE, supports multi-party interaction, no trusted third party needed.

• Limitations: High communication overhead and network latency issues.

Comparative Summary

Future Directions and Challenges

• Hybrid Approaches: Use HE for encrypted data storage and SMPC for efficient multi-party computation.

• Threshold Homomorphic Encryption: Combines HE with secret sharing for decentralized trust models.

• Performance Improvements:

  • Hardware acceleration with FPGAs, ASICs, GPUs.

  • Algorithmic advances to reduce bootstrapping time and communication rounds.

• Challenges: FHE remains slow; SMPC suffers from network latency in large-scale scenarios.

Conclusion

Homomorphic encryption and secure multi-party computation represent groundbreaking advances in privacy-preserving computation. While HE excels in single-party encrypted processing, SMPC is better suited for collaborative computations among multiple entities. Both face challenges in performance and scalability, but ongoing research in optimization, hardware acceleration, and hybrid models holds promise for wider adoption. As data privacy regulations tighten, these technologies will play a crucial role in secure data processing across industries.

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Copyright

Copyright © 2025 Abbas Abubaker Mohammed Ahessin, Ali Mohammed Omar Ali . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.