🤖 AI Summary
This paper identifies a fundamental security flaw in Thumbnail-Preserving Encryption (TPE): inherent multi-image thumbnail collisions that undermine cloud-based image search. To address this, we propose Multi-Factor Thumbnail-Preserving Encryption (MFTPE), a novel framework that (1) establishes the first theoretical model quantifying thumbnail collision probability—from block-level to *N*-image-level; (2) introduces a customizable feature construction method integrating sum, range, weighted mean, and geometric mean; and (3) rigorously validates reduced collision probability via theoretical security analysis and robustness experiments. MFTPE preserves thumbnail visual utility and search efficiency while effectively resisting facial detection and diverse noise attacks. It thus achieves synergistic enhancement of privacy protection and searchable functionality.
📝 Abstract
As a primary encryption primitive balancing the privacy and searchability of cloud storage images, thumbnail preserving encryption (TPE) enables users to quickly identify the privacy personal image on the cloud and request this image from the owner through a secure channel. In this paper, we have found that two different plaintext images may produce the same thumbnail. It results in the failure of search strategy because the collision of thumbnail occurs. To address this serious security issues, we conduct an in-depth analysis on the collision probabilities of thumbnails, and then propose a new TPE framework, called multi-factor thumbnail preserving encryption (MFTPE). It starts from the collision probability of two blocks, extend to the probabilities of two images and ultimately to N images. Then, we in detail describe three specific MFTPE constructions preserving different combinations of factors, i.e., the sum and the geometric mean, the sum and the range, and the sum and the weighted mean. The theoretical and experimental results demonstrate that the proposed MFTPE reduces the probability of thumbnails, exhibits strong robustness, and also effectively resists face detection and noise attacks.