Randomized moduli in Residue Number System (RNS) generate effectively large noise and make quite difficult to attack a secret key $K$ from only few observations of Hamming distances $H=(H_0, ..., H_{d-1})$ that result from the changes on the state variable. Since Hamming distances have gaussian distribution and most of the statistic tests, like NIST's ones, evaluate discrete and uniform distribution, we choose to use side-channel attacks as a tool in order to evaluate randomisation of Hamming distances . This paper analyses the resilience against Correlation Power Analysis (CPA), Differential Power Analysis (DPA) when the cryptographic system is protected against Simple Power Analysis (SPA) by a Montgomery Powering Ladder (MPL). While both analysis use only information on the current state, DPA Square crosses the information of all the states. We emphasize that DPA Square performs better than DPA and CPA and we show that the number of observations $S$ needed to perform an attack increases with respect to the number of moduli $n$. For Elliptic Curves Cryptography (ECC) and using a Monte Carlo simulation, we conjecture that $S = O((2n)!/(n!)^2)$.

Decision of whether a Boolean equation system has a solution is an NPC problem and finding a solution is NP hard. In this paper, we present a quantum algorithm to decide whether a Boolean equation system F has a solution and compute one if F does have solutions with any given success probability. The complexity of the algorithm is polynomial in the size of F and the condition number of F. As a consequence, we have achieved exponential speedup for solving sparse Boolean equation systems if their condition numbers are small. We apply the quantum algorithm to the cryptanalysis of the stream cipher Trivum, the block cipher AES, the hash function SHA-3/Keccak, and the multivariate public key cryptosystems, and show that they are secure under quantum algebraic attack only if the condition numbers of the corresponding equation systems are large.

How to efficiently search over encrypted data is an important and interesting problem in the cloud era. To solve it, Boneh et al. introduced the notion of public key encryption with keyword search (PEKS), in 2004. However, in almost all the PEKS schemes an inside adversary may recover the keyword from a given trapdoor by exhaustively guessing the keywords offline. How to resist the inside keyword guessing attack in PEKS remains a hard problem. In this paper we propose introduce the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS) to solve the problem, in which the data sender not only encrypts a keyword, but also authenticates it, so that a verifier would be convinced that the encrypted keyword can only be generated by the sender. We propose a concrete and efficient construction of PAEKS, and prove its security based on simple and static assumptions in the random oracle model under the given security models. Experimental results show that our scheme enjoys a comparable efficiency with Boneh et al.'s scheme.

Masking schemes represent a well-researched and successful option to follow when considering side-channel countermeasures. Still, such measures increase the implementation cost in term of power consumption, clock cycles, and random numbers generation. In fact, the higher the order of protection against side-channel adversaries, the higher the implementation cost of countermeasures. S-boxes represent the most vulnerable part in an implementation when considering side-channel adversary. In this paper, we investigate how to generate S-boxes that have improved resilience against varying orders of side-channel attacks while minimising the implementation costs. We examine whether S-boxes generated against a certain order of attack also represent a good solution when considering different order of attacks. We demonstrate that we successfully generated S-boxes resilient against a certain physical attack order but the improvements are small. As a result, S-boxes that are resilient against first order attacks stay resilient against higher-order attacks, which saves computational power during the design of higher-order side-channel attacks resilient S-boxes.

We show a protocol for two-server oblivious RAM (ORAM) that is simpler and more efficient than the best prior work. Our construction combines any tree-based ORAM with an extension of a two-server private information retrieval scheme by Boyle et al., and is able to avoid recursion and thus use only one round of interaction. In addition, our scheme has a very cheap initialization phase, making it well suited for RAM-based secure computation. Although our scheme requires the servers to perform a linear scan over the entire data, the cryptographic computation involved consists only of block-cipher evaluations. A practical instantiation of our protocol has excellent concrete parameters: for storing an $N$-element array of arbitrary size data blocks with statistical security parameter $\lambda$, the servers each store $4N$ encrypted blocks, the client stores $\lambda+2\log N$ blocks, and the total communication per logical access is roughly $10 \log N$ encrypted blocks.

In this paper, we ask a question whether convolutional neural networks are more suitable for SCA scenarios than some other machine learning techniques, and if yes, in what situations. Our results point that convolutional neural networks indeed outperforms machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for convolutional neural networks only when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similar or even better. The experiments with the guessing entropy metric indicate that simpler methods like Random forest or XGBoost perform better than convolutional neural networks for the datasets we investigated. Finally, we conduct a small experiment that opens the question whether convolutional neural networks are actually the best choice in side-channel analysis context since there seems to be no advantage in preserving the topology of measurements.

Bad choices of passwords were and are a pervasive problem. Users choosing weak passwords do not only compromise themselves, but the whole ecosystem. E.g, common and default passwords in IoT devices were exploited by hackers to create botnets and mount severe attacks on large Internet services, such as the Mirai botnet DDoS attack. We present a method to help protect the Internet from such large scale attacks. Our method enables a server to identify popular passwords (heavy hitters), and publish a list of over-popular passwords that must be avoided. This filter ensures that no single password can be used to compromise a large percentage of the users. The list is dynamic and can be changed as new users are added or when current users change their passwords. We apply maliciously secure two-party computation and differential privacy to protect the users' password privacy. Our solution does not require extra hardware or cost, and is transparent to the user. Our private heavy hitters construction is secure even against a malicious coalition of devices which tries to manipulate the protocol to hide the popularity of some password that the attacker is exploiting. It also ensures differential privacy under continual observation of the blacklist as it changes over time. As a reality check we conducted three tests: computed the guarantees that the system provides wrt a few publicly available databases, ran full simulations on those databases, and implemented and analyzed a proof-of-concept on an IoT device. Our construction can also be used in other settings to privately learn heavy hitters in the presence of an active malicious adversary. E.g., learning the most popular sites accessed by the Tor network.

The multiplicative complexity of a Boolean function is the minimum number of AND gates that are necessary and sufficient to implement the function over the basis (AND, XOR, NOT). Finding the multiplicative complexity of a given function is computationally intractable, even for functions with small number of inputs. Turan et al. showed that $n$-variable Boolean functions can be implemented with at most $n-1$ AND gates for $n\leq 5$. A counting argument can be used to show that, for $n \geq 7$, there exist $n$-variable Boolean functions with multiplicative complexity of at least $n$. In this work, we propose a method to find the multiplicative complexity of Boolean functions by analyzing circuits with a particular number of AND gates and utilizing the affine equivalence of functions. We use this method to study the multiplicative complexity of 6-variable Boolean functions, and calculate the multiplicative complexities of all 150357 affine equivalence classes. We show that any 6-variable Boolean function can be implemented using at most 6 AND gates. Additionally, we exhibit specific 6-variable Boolean functions which have multiplicative complexity 6.

Consider the following secret-sharing problem. Your goal is to distribute a long file $s$ between $n$ servers such that $(d-1)$-subsets cannot recover the file, $(d+1)$-subsets can recover the file, and $d$-subsets should be able to recover $s$ if and only if they appear in some predefined list $L$. How small can the information ratio (i.e., the number of bits stored on a server per each bit of the secret) be? We initiate the study of such $d$-uniform access structures, and view them as a useful scaled-down version of general access structures. Our main result shows that, for constant $d$, any $d$-uniform access structure admits a secret sharing scheme with a *constant* asymptotic information ratio of $c_d$ that does not grow with the number of servers $n$. This result is based on a new construction of $d$-party Conditional Disclosure of Secrets (Gertner et al., JCSS '00) for arbitrary predicates over $n$-size domain in which each party communicates at most four bits per secret bit. In both settings, previous results achieved non-constant information ratio which grows asymptotically with $n$ even for the simpler (and widely studied) special case of $d=2$. Moreover, our results provide a unique example for a natural class of access structures $F$ that can be realized with information rate smaller than its bit-representation length $\log |F|$ (i.e., $\Omega( d \log n)$ for $d$-uniform access structures) showing that amortization can beat the representation size barrier. Our main result applies to exponentially long secrets, and so it should be mainly viewed as a barrier against amortizable lower-bound techniques. We also show that in some natural simple cases (e.g., low-degree predicates), amortization kicks in even for quasi-polynomially long secrets. Finally, we prove some limited lower-bounds, point out some limitations of existing lower-bound techniques, and describe some applications to the setting of private simultaneous messages.