Joint data and key distribution of simple, multiple, and multidimensional linear cryptanalysis test statistic and its impact to data complexity

详细信息    查看全文

• 作者：Céline Blondeau ; Kaisa Nyberg
• 关键词：Iterated block cipher ; Linear attack ; Known plaintext ; Distinct known plaintext ; Key variance ; Statistical model
• 刊名：Designs, Codes and Cryptography
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：82
• 期：1-2
• 页码：319-349
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Combinatorics; Coding and Information Theory; Data Structures, Cryptology and Information Theory; Data Encryption; Discrete Mathematics in Computer Science; Information and Communication, Circuits;
• 出版者：Springer US
• ISSN：1573-7586
• 卷排序：82

文摘

The power of a statistical attack is inversely proportional to the number of plaintexts needed to recover information on the encryption key. By analyzing the distribution of the random variables involved in the attack, cryptographers aim to provide a good estimate of the data complexity of the attack. In this paper, we analyze the hypotheses made in simple, multiple, and multidimensional linear attacks that use either non-zero or zero correlations, and provide more accurate estimates of the data complexity of these attacks. This is achieved by taking, for the first time, into consideration the key variance of the statistic for both the right and wrong keys. For the family of linear attacks considered in this paper, we differentiate between the attacks which are performed in the known-plaintext and those in the distinct-known-plaintext model.