Comparing Large-unit and Bitwise Linear Approximations of SNOW 2.0 and SNOW 3G and Related Attacks

In this paper, we study and compare the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G, and present a fast correlation attack on SNOW 3G by using our newly found bitwise linear approximations. On one side, we reconsider the relation between the large-unit linear approximation and the smallerunit/ bitwise ones derived from the large-unit one, showing that approximations on large-unit alphabets have advantages over all the smaller-unit/bitwise ones in linear attacks. But then on the other side, by comparing the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G respectively, we have found many concrete examples of 8-bit linear approximations whose certain 1-dimensional/bitwise linear approximations have almost the same SEI (Squared Euclidean Imbalance) as that of the original 8-bit ones. That is, each of these byte-wise linear approximations is dominated by a single bitwise approximation, and thus the whole SEI is not essentially larger than the SEI of the dominating single bitwise approximation. Since correlation attacks can be more efficiently implemented using bitwise approximations rather than large-unit approximations, improvements over the large-unit linear approximation attacks are possible for SNOW 2.0 and SNOW 3G. For SNOW 3G, we make a careful search of the bitwise masks for the linear approximations of the FSM and obtain many mask tuples which yield high correlations. By using these bitwise linear approximations, we mount a fast correlation attack to recover the initial state of the LFSR with the time/memory/data/pre-computation complexities all upper bounded by 2174.16, improving slightly the previous best one which used an 8-bit (vectorized) linear approximation in a correlation attack with all the complexities upper bounded by 2176.56. Though not a significant improvement, our research results illustrate that we have an opportunity to achieve improvement over the large-unit attacks by using bitwise linear approximations in a linear approximation attack, and provide a newinsight on the relation between large-unit and bitwise linear approximations.

Resistance of SNOW-V against Fast Correlation Attacks

SNOW-V is a new member in the SNOW family of stream ciphers, hoping to be competitive in the 5G mobile communication system. In this paper, we study the resistance of SNOW-V against bitwise fast correlation attacks by constructing bitwise linear approximations. First, we propose and summarize some efficient algorithms using the slice-like techniques to compute the bitwise linear approximations of certain types of composition functions composed of basic operations like ⊞, ⊕, Permutation, and S-box, which have been widely used in word-oriented stream ciphers such as SNOW-like ciphers. Then, using these algorithms, we find a number of stronger linear approximations for the FSM of the two variants of SNOW-V given in the design document, i.e., SNOW-V σ0 and SNOW-V⊞8, ⊞8. For SNOW-V σ0, where there is no byte-wise permutation, we find some bitwise linear approximations of the FSM with the SEI (Squared Euclidean Imbalance) around 2−37.34 and mount a bitwise fast correlation attack with the time complexity 2251.93 and memory complexity 2244, given 2103.83 keystream outputs, which improves greatly the results in the design document. For SNOW-V⊞8, ⊞8, where both of the two 32-bit adders in the FSM are replaced by 8-bit adders, we find our best bitwise linear approximations of the FSM with the SEI 2−174.14, while the best byte-wise linear approximation in the design document of SNOW-V has the SEI 2−214.80. Finally, we study the security of a closer variant of SNOW-V, denoted by SNOW-V⊞32, ⊞8, where only the 32-bit adder used for updating the first register is replaced by the 8-bit adder, while everything else remains identical. For SNOW-V⊞32, ⊞8, we derive many mask tuples yielding the bitwise linear approximations of the FSM with the SEI larger than 2−184. Using these linear approximations, we mount a fast correlation attack with the time complexity 2377.01 and a memory complexity 2363, given 2253.73 keystream outputs. Note that neither of our attack threatens the security of SNOW-V. We hope our research could further help in understanding bitwise linear approximation attacks and also the structure of SNOW-like stream ciphers.

A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy

In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

Enhancing Image Security during Transmission using Residue Number System and k-shuffle

This paper proposes an algorithm that enhances the speed of transmission and secure images that are transmitted over internet or a network. The proposed cryptosystem uses a modified k-shuffling technique to scramble pixels of images and further decomposes them using Residue Number System. Simulations are done using two moduli sets with the modified k-shuffle technique. Analyses of results showed that both simulations could secure images without any loss of information and also the time taken for a complete encryption/decryption process is dependent on the moduli set. Among the chosen moduli sets, the even moduli set optimizes and completes execution using less time as compared to the traditional moduli set. The proposed scheme also showed resistance to statistical attacks (histogram, ciphertext, correlation attacks) and a significant reduction in the size of cipher images which enhances the speed of transmission over network.

Guard Placement Attacks on Path Selection Algorithms for Tor

The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack. We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84× higher than what it would be under Tor currently. Our findings indicate that existing location-based path selection algorithms allow guards to achieve disproportionately high selection probabilities relative to the cost required to run the guard. Finally, we propose and evaluate a generic defense mechanism that provably defends any path selection algorithm against guard placement attacks. We run our defense mechanism on each of the three path selection algorithms, and find that our mechanism significantly enhances the security of these algorithms against guard placement attacks with only minimal impact to the goals or performance of the original algorithms.

Address Space Layout Randomization Next Generation

Systems that are built using low-power computationally-weak devices, which force developers to favor performance over security; which jointly with its high connectivity, continuous and autonomous operation makes those devices specially appealing to attackers. ASLR (Address Space Layout Randomization) is one of the most effective mitigation techniques against remote code execution attacks, but when it is implemented in a practical system its effectiveness is jeopardized by multiple constraints: the size of the virtual memory space, the potential fragmentation problems, compatibility limitations, etc. As a result, most ASLR implementations (specially in 32-bits) fail to provide the necessary protection. In this paper we propose a taxonomy of all ASLR elements, which categorizes the entropy in three dimensions: (1) how, (2) when and (3) what; and includes novel forms of entropy. Based on this taxonomy we have created, ASLRA, an advanced statistical analysis tool to assess the effectiveness of any ASLR implementation. Our analysis show that all ASLR implementations suffer from several weaknesses, 32-bit systems provide a poor ASLR, and OS X has a broken ASLR in both 32- and 64-bit systems. This is jeopardizing not only servers and end users devices as smartphones but also the whole IoT ecosystem. To overcome all these issues, we present ASLR-NG, a novel ASLR that provides the maximum possible absolute entropy and removes all correlation attacks making ASLR-NG the best solution for both 32- and 64-bit systems. We implemented ASLR-NG in the Linux kernel 4.15. The comparative evaluation shows that ASLR-NG overcomes PaX, Linux and OS X implementations, providing strong protection to prevent attackers from abusing weak ASLRs.