Unboxing ARX-Based White-Box Ciphers: Chosen-Plaintext Computation Analysis and Its Applications

Abstract

It has been proven that the white-box ciphers with small encodings will be vulnerable to algebraic and computation attacks. By leveraging the large encodings, the self-equivalence and implicit implementations are proposed for ARXbased white-box ciphers. Unfortunately, these two types of white-box implementations are proven to be insecure under the algebraic attack. Different from algebraic attacks, computation analysis can extract the secret key from the memory access traces without software reverse engineering. It is still an open problem whether the self-equivalence and implicit implementations can resist the computation analysis. In this paper, we analyze the encoded structure of the self-equivalence/implicit whitebox ARX ciphers and discuss its resistance to the computation analysis, such as differential computation analysis (DCA) and algebraic degree computation analysis (ADCA). The results reveal that the large input, encoding, and round key can practically mitigate DCA and ADCA. To deal with the large space, we introduce a new method which is named chosen-plaintext computation analysis (CP-CA). Based on a partial key guess and deliberately chosen intermediate value, CP-CA constructs a reverse function to calculate a set of plaintexts. With the obtained plaintexts, the large affine and non-linear encodings will be reduced to a small space. Subsequently, CP-CA mounts the computation analysis on the traces to recover the secret key. Following CP-CA, we propose a CP-DCA attack and reformulate ADCA as chosen-plaintext linear encoding analysis (CP-LEA). The experimental results indicate that the selfequivalence white-box SPECK32/48/64/96/128 and implicit white-box SPECK32/64 implementations are vulnerable to CP-DCA and CP-LEA attacks.

Keywords

• White-box implementation
• Self-equivalence encoding
• Implicit function
• Differential computation analysis
• Algebraic degree computation analysis