Title | Light-weight white-box encryption scheme with random padding for wearable consumer electronic devices |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Shi, Yang, Wang, Xiaoping, Fan, Hongfei |
Journal | IEEE Transactions on Consumer Electronics |
Volume | 63 |
Pagination | 44–52 |
ISSN | 1558-4127 |
Keywords | composability, Consumer electronics, Context, cryptography, cryptosystem, data mining, decryption algorithms, Encryption, lightweight white box encryption scheme, limited memory, low computational power, Metrics, performance evaluation, physical nature, privacy protection, pubcrawl, random padding, random secret components, Resiliency, wearable computers, wearable computing, wearable consumer electronic devices, wearable devices, wearabledevices, white box cryptography, white-box attack contexts, white-box attacks |
Abstract | Wearable devices can be potentially captured or accessed in an unauthorized manner because of their physical nature. In such cases, they are in white-box attack contexts, where the adversary may have total visibility on the implementation of the built-in cryptosystem, with full control over its execution platform. Dealing with white-box attacks on wearable devices is undoubtedly a challenge. To serve as a countermeasure against threats in such contexts, we propose a lightweight encryption scheme to protect the confidentiality of data against white-box attacks. We constructed the scheme's encryption and decryption algorithms on a substitution-permutation network that consisted of random secret components. Moreover, the encryption algorithm uses random padding that does not need to be correctly decrypted as part of the input. This feature enables non-bijective linear transformations to be used in each encryption round to achieve strong security. The required storage for static data is relatively small and the algorithms perform well on various devices, which indicates that the proposed scheme satisfies the requirements of wearable computing in terms of limited memory and low computational power. |
DOI | 10.1109/TCE.2017.014722 |
Citation Key | shi_light-weight_2017 |