Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing
Nguyen Van Duc, Bui Duc Manh, Quang-Trung Luu, Dinh Thai Hoang, Van-Linh Nguyen, and Diep N. Nguyen

TL;DR
This paper introduces a secure UAV-based face detection framework that combines homomorphic encryption with neural networks, ensuring privacy without significantly sacrificing detection accuracy.
Contribution
It presents a novel integration of homomorphic encryption with neural networks for privacy-preserving face detection in UAVs, optimizing both security and efficiency.
Findings
Achieves less than 1% accuracy loss compared to non-encrypted benchmarks.
Uses CKKS scheme for efficient encrypted computations.
Develops a data encoding method for fast encrypted data processing.
Abstract
This paper aims to propose a novel machine learning (ML) approach incorporating Homomorphic Encryption (HE) to address privacy limitations in Unmanned Aerial Vehicles (UAV)-based face detection. Due to challenges related to distance, altitude, and face orientation, high-resolution imagery and sophisticated neural networks enable accurate face recognition in dynamic environments. However, privacy concerns arise from the extensive surveillance capabilities of UAVs. To resolve this issue, we propose a novel framework that integrates HE with advanced neural networks to secure facial data throughout the inference phase. This method ensures that facial data remains secure with minimal impact on detection accuracy. Specifically, the proposed system leverages the Cheon-Kim-Kim-Song (CKKS) scheme to perform computations directly on encrypted data, optimizing computational efficiency and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
