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EN
Data compression combined with effective encryption is a common requirement of data storage and transmission. Low cost of these operations is often a high priority in order to increase transmission speed and reduce power usage. This requirement is crucial for battery-powered devices with limited resources, such as autonomous remote sensors or implants. Well-known and popular encryption techniques are frequently too expensive. This problem is on the increase as machine-to-machine communication and the Internet of Things are becoming a reality. Therefore, there is growing demand for finding trade-offs between security, cost and performance in lightweight cryptography. This article discusses asymmetric numeral systems-an innovative approach to entropy coding which can be used for compression with encryption. It provides a compression ratio comparable with arithmetic coding at a similar speed as Huffman coding; hence, this coding is starting to replace them in new compressors. Additionally, by perturbing its coding tables, the asymmetric numeral system makes it possible to simultaneously encrypt the encoded message at nearly no additional cost. The article introduces this approach and analyzes its security level. The basic application is reducing the number of rounds of some cipher used on ANS-compressed data, or completely removing an additional encryption layer when reaching a satisfactory protection level.
EN
The IoT technology is currently used in many areas and marked by growing popularity. On the one hand, the IoT makes our lives easier, on the other hand, it presents challenges in terms of security and privacy protection. An IoT infrastructure is characterized by a high level of threats due to, inter alia, numerous technical barriers that make it difficult to use conventional methods to protect information. The aim of this paper is to present a symmetric coding algorithm based on algebraic groups generated by specific trigonometric curves. The algorithm is dedicated to short data sequences transmitted by devices with limited computing power.
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