On the Security of Block Scrambling-Based EtC Systems against Extended Jigsaw Puzzle Solver Attacks

Abstract

SUMMARYThe aim of this paper is to apply automatic jigsaw puzzle solvers, which are methods of assembling jigsaw puzzles, to the field of information security. Encryption-then-Compression (EtC) systems have been considered for the user-controllable privacy protection of digital images in social network services. Block scrambling-based encryption schemes, which have been proposed to construct EtC systems, have enough key spaces for protecting brute-force attacks. However, each block in encrypted images has almost the same correlation as that of original images. Therefore, it is required to consider the security from different viewpoints from number theory-based encryption methods with provable security such as RSA and AES. In this paper, existing jigsaw puzzle solvers, which aim to assemble puzzles including only scrambled and rotated pieces, are first reviewed in terms of attacking strategies on encrypted images. Then, an extended jigsaw puzzle solver for block scrambling-based encryption scheme is proposed to solve encrypted images including inverted, negative-positive transformed and color component shuffled blocks in addition to scrambled and rotated ones. In the experiments, the jigsaw puzzle solvers are applied to encrypted images to consider the security conditions of the encryption schemes.

Privacy-Preserving Deep Neural Networks with Pixel-Based Image Encryption Considering Data Augmentation in the Encrypted Domain

“…Compared with full encryption with provable security like homomorphic encryption (HE), they generally have a low computational cost and can offer encrypted data robust against various kinds of noise and errors. In addition, some of them aim to consider both security and efficient compression so that they can be adapted to cloud storage and network sharing [9][10][11][12][13][14][15]. However, with the exception of a few previous pieces of work, most conventional perceptual encryption methods have never been considered for application to machine learning algorithms [5,6].…”

Bitstream-Based JPEG Image Encryption with File-Size Preserving

“…Encryption then Compression(EtC) systems [1]- [11] are methods to encrypt for images before encoding. Some of these methods have the compatibility with international compression standards, and enable privacy preserving decompression and compression.The compression performances of EtC systems are almost as same as one of the original images, but the file sizes are slightly different from those of compressed images without encryption.…”

Privacy-Preserving SVM Computing by Using Random Unitary Transformation

“…For securing data, full encryption with provable security (like RSA, AES, etc) is the most secure option. However, many multimedia applications have been seeking a trade-off in security to enable other requirements, e.g., low processing demands, retaining bitstream compliance, and flexible processing in the encrypted domain, so that a lot of perceptual encryption schemes have been studied as one of the schemes for achieving a trade-off [4]- [13] In the recent years, considerable efforts have been made in the fields of fully homomorphic encryption and multi-party computation [14]- [17]. However, these schemes can not be applied yet to SVM algorithms, although it is possible to carry out some statistical analysis of categorical and ordinal data.…”