Proof of Mirror Theory for a Wide Range of ξmax⁡\xi_{\max}

In CRYPTO\u2703, Patarin conjectured a lower bound on the number of distinct solutions $(P_1, \ldots, P_{q}) \in (\{0, 1\}^{n})^{q}$ satisfying a system of equations of the form $X_i \oplus X_j = \lambda_{i,j}$ such that $P_1, P_2, \ldots$, $P_{q}$ are pairwise distinct. This result is known as \emph{``$P_i \oplus P_j$ Theorem for any $\xi_{\max}$\u27\u27} or alternatively as \emph{Mirror Theory for general $\xi_{\max}$}, which was later proved by Patarin in ICISC\u2705. Mirror theory for general $\xi_{\max}$ stands as a powerful tool to provide a high-security guarantee for many blockcipher-(or even ideal permutation-) based designs. Unfortunately, the proof of the result contains gaps that are non-trivial to fix. In this work, we present the first complete proof of the $P_i \oplus P_j$ theorem for a wide range of $\xi_{\max}$, typically up to order $O(2^{n/4}/\sqrt{n})$. Furthermore, our proof approach is made simpler by using a new type of equation, dubbed link-deletion equation, that roughly corresponds to half of the so-called orange equations from earlier works. As an illustration of our result, we also revisit the security proofs of two optimally secure blockcipher-based pseudorandom functions, and $n$-bit security proof for six round Feistel cipher, and provide updated security bounds

Rigorous Upper Bounds on Data Complexities of Block Cipher Cryptanalysis

Statistical analysis of symmetric key attacks aims to obtain an expression for the data complexity which is the number of plaintext-ciphertext pairs needed to achieve the parameters of the attack. Existing statistical analyses invariably use some kind of approximation, the most common being the approximation of the distribution of a sum of random variables by a normal distribution. Such an approach leads to expressions for data complexities which are {\em inherently approximate}. Prior works do not provide any analysis of the error involved in such approximations. In contrast, this paper takes a rigorous approach to analysing attacks on block ciphers. In particular, no approximations are used. Expressions for upper bounds on the data complexities of several basic and advanced attacks are obtained. The analysis is based on the hypothesis testing framework. Probabilities of Type-I and Type-II errors are upper bounded using standard tail inequalities. In the cases of single linear and differential cryptanalysis, we use the Chernoff bound. For the cases of multiple linear and multiple differential cryptanalysis, Hoeffding bounds are used. This allows bounding the error probabilities and obtaining expressions for data complexities. We believe that our method provides important results for the attacks considered here and more generally, the techniques that we develop should have much wider applicability

On the Price of Proactivizing Round-Optimal Perfectly Secret Message Transmission

In a network of $n$ nodes (modelled as a digraph), the goal of a perfectly secret message transmission (PSMT) protocol is to replicate sender\u27s message $m$ at the receiver\u27s end without revealing any information about $m$ to a computationally unbounded adversary that eavesdrops on any $t$ nodes. The adversary may be mobile too -- that is, it may eavesdrop on a different set of $t$ nodes in different rounds. We prove a necessary and sufficient condition on the synchronous network for the existence of $r$-round PSMT protocols, for any given $r > 0$; further, we show that round-optimality is achieved without trading-off the communication complexity; specifically, our protocols have an overall communication complexity of $O(n)$ elements of a finite field to perfectly transmit one field element. Apart from optimality/scalability, two interesting implications of our results are: (a) adversarial mobility does not affect its tolerability: PSMT tolerating a static $t$-adversary is possible if and only if PSMT tolerating mobile $t$-adversary is possible; and (b) mobility does not affect the round optimality: the fastest PSMT protocol tolerating a static $t$-adversary is not faster than the one tolerating a mobile $t$-adversary

Multidimensional linear cryptanalysis

Linear cryptanalysis is an important tool for studying the security of symmetric ciphers. In 1993 Matsui proposed two algorithms, called Algorithm 1 and Algorithm 2, for recovering information about the secret key of a block cipher. The algorithms exploit a biased probabilistic relation between the input and output of the cipher. This relation is called the (one-dimensional) linear approximation of the cipher. Mathematically, the problem of key recovery is a binary hypothesis testing problem that can be solved with appropriate statistical tools. The same mathematical tools can be used for realising a distinguishing attack against a stream cipher. The distinguisher outputs whether the given sequence of keystream bits is derived from a cipher or a random source. Sometimes, it is even possible to recover a part of the initial state of the LFSR used in a key stream generator. Several authors considered using many one-dimensional linear approximations simultaneously in a key recovery attack and various solutions have been proposed. In this thesis a unified methodology for using multiple linear approximations in distinguishing and key recovery attacks is presented. This methodology, which we call multidimensional linear cryptanalysis, allows removing unnecessary and restrictive assumptions. We model the key recovery problems mathematically as hypothesis testing problems and show how to use standard statistical tools for solving them. We also show how the data complexity of linear cryptanalysis on stream ciphers and block ciphers can be reduced by using multiple approximations. We use well-known mathematical theory for comparing different statistical methods for solving the key recovery problems. We also test the theory in practice with reduced round Serpent. Based on our results, we give recommendations on how multidimensional linear cryptanalysis should be used