Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach

Conference · Mon Oct 14 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/ICWS62655.2024.00061· OSTI ID:2526242

Khan, Muhammad Jahanzeb ^[1]; Fang, Bo ^[2]; Cimino, Gaetano ^[3]; Cirillo, Stefano ^[3]; Yang, Lei ^[1]; Zhao, Dongfang ^[4]

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1. University of Nevada - Reno
2. BATTELLE (PACIFIC NW LAB)
3. University of Salerno
4. University of Washington

Recent advancements in privacy-preserving artificial intelligence (AI) have paved the way for enhanced privacy in computational processes. A standing challenge, however, is the robust privacy preservation in AI algorithms, especially when integrated into edge devices and Internet-of-Thing (IoT) infrastructures. Most prevailing solutions have adopted traditional encryption methods which, though secure, often introduce significant overhead and potential dips in accuracy. In this study, we put forth an innovative approach, utilizing the CKKS encryption scheme, aiming to harmoniously balance computational efficiency with stringent data privacy. By harnessing the capabilities of Full Homomorphic Encryption (FHE) under the CKKS scheme, we ensure the preservation of privacy, successfully curbing the inherent noise traditionally linked with accuracy reductions in similar encryption-oriented solutions. Through comprehensive experiments, our approach showcased its potential as a strong contender for privacy preservation, demonstrating commendable performance across all tests, affirming that FHE is indeed viable for devices with constrained computational power and energy resources.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2526242
• Report Number(s): PNNL-SA-198534
• Country of Publication: United States
• Language: English