Concurrent encryption and lossless compression using inversion ranks

• Published in: Journal of information security and applications 2023-11, Vol.78, p.103587, Article 103587
• Main Authors: Koc, Basar, Arnavut, Ziya, Koçak, Hüseyin
• Format: Article
• Language: English
• Subjects: Burrows–Wheeler transformation; Data security; Inversion ranking; Lossless data compression; Multimedia security
• ISSN: 2214-2126
• DOI: 10.1016/j.jisa.2023.103587

For secure and efficient transmission or storage, data files are commonly compressed and encrypted. In this work, we introduce a cost-effective encryption method of files as a built-in component of a lossless compression algorithm, thus avoiding the added cost of employing two separate processes. We have shown in earlier studies that preprocessing data with Burrows–Wheeler Transformation followed by Inversion Ranking transformation in advance of the utilization of an entropy coder resulted in an extremely effective general-purpose lossless compression technique. In our proposed algorithm, we isolate the inversion frequency vector obtained from the Inversion Ranking transformation and transmit the compressed file. Since the inversion frequency vector is necessary for decompression, we use it as our secret key and transmit it over a secure channel. Compared to the size of a data file, its frequency vector is rather small. Thus, encrypting only the inversion frequency vector, instead of the entire compressed file, results in substantial savings in computational cost. We demonstrate that the proposed technique is effective and resistant to common attacks on image and audio data sets. [Display omitted] •BWIC is an effective general-purpose lossless data compression algorithm.•Each input data set generates its unique inversion frequency vector, thus a unique secret key.•Our proposed technique does not negatively affect compression efficiency.•Compared to the size of a data file, its frequency vector is rather small.•Resistance of our algorithm to common attacks is demonstrated on image and audio data sets.