Collision Attack Research Articles

Classical and Quantum Meet-in-the-Middle Nostradamus Attacks on AES-like Hashing

At EUROCRYPT 2006, Kelsey and Kohno proposed the so-called chosen target forced-prefix (CTFP) preimage attack, where for any challenge prefix P, the attacker can generate a suffix S such that H(P∥S) = y for some hash value y published in advance by the attacker. Consequently, the attacker can pretend to predict some event represented by P she did not know before, and thus this type of attack is also known as the Nostradamus attack. At ASIACRYPT 2022, Benedikt et al. convert Kelsey et al.’s attack to a quantum one, reducing the time complexity from O(√n · 22n/3) to O( 3√n · 23n/7). CTFP preimage attack is less investigated in the literature than (second-)preimage and collision attacks and lacks dedicated methods. In this paper, we propose the first dedicated Nostradamus attack based on the meet-in-the-middle (MITM) attack, and the MITM Nostradamus attack could be up to quadratically accelerated in the quantum setting. According to the recent works on MITM preimage attacks on AES-like hashing, we build an automatic tool to search for optimal MITM Nostradamus attacks and model the tradeoff between the offline and online phases. We apply our method to AES-MMO and Whirlpool, and obtain the first dedicated attack on round-reduced version of these hash functions. Our method and automatic tool are applicable to other AES-like hashings.

Efficient multi-party private set intersection protocols for large participants and small sets

Private set intersection (PSI) has attracted the researchers and the developers both from academia and industry in cooperating the private data to achieve privacy preserving. Traditional PSI protocols allow the two participants with each private sets interactively calculating the intersection without revealing any additional information. Multi-party private set intersection (MP-PSI) is quite different from the conventional two-party PSI, which could not directly obtain from the two-party PSI, and become an emerging topic in collaborative data analytics from different private data owners. However, the most of the existing MP-PSI protocols always consider the small number of the participants with large set size and the performance drops with the increasing number of participants. In addition, the participants with small set size also bring the heavy communication and computation overhead. To overcome the above drawbacks, we propose two efficient MP-PSI protocols for large number of participants and small set size, and formally prove secure against collision attack in the semi-honest model and malicious model. The experiments show that when the participant number increases from 5 to 50 and the set size scales from 27 to 210, our protocols are faster than the most efficient MP-PSI protocol. Thus, our MP-PSI protocols are considering to be practical solutions, which fit the scenarios in small set size with increasing participants.

A White-box Implementation of SM4 with Self-equivalence Encoding

Abstract White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to protect the key. However, with the development of white-box attack techniques, many white-box implementations have been proved to be insecure. In this paper, a new white-box implementation of SM4 is proposed, which is based on an equivalent partial SPN structure of the SM4 algorithm. Our implementation includes three types of table lookup operations and XOR operations. The round keys are obfuscated with the self-equivalences of the S-box and random affine encodings. Security analysis shows that our implementation can resist BGE-type attack, the attack based on affine equivalence algorithm, the structure attack, the collision attack and differential computational analysis. Furthermore, our scheme requires 8.125 MB of memory.

A privacy preservation data collection and access control using entropy-based conic curve

Because significantly complex crypto procedures such as holomorphic encryption are robotically applied, despite the fact that consumer gadgets under our software circumstances are not, computational overhead is outrageously high. Simply hiding customers with the aid of nameless communications to act to protect the server and adversaries from linking suggestions made with the aid of the same customer makes the traditional method, which computes with the aid of any server based on the amount of provided services, impossible, and customers with charge features widely publicised with the aid of the server cause additional security concerns, impossible. To overcome the above existing drawbacks, this research study presents a Privacy Preservation Data Collection and Access Control Using Entropy-Based Conic Curve. To safeguard the identity of clients and their requests, EBCC employs a unique group signature technic and an asymmetric cryptosystem. First, we ought to implement our EBCC method for data acquisition while maintaining privacy. Second, we consider looking at the properties of secure multiparty computation. EBCC employs lightweight techniques in encryption, aggregation, and decryption, resulting in little computation and communication overhead. Security research suggests that the EBCC is safe, can withstand collision attacks, and can conceal consumer distribution, which is required for fair balance checks in credit card payments. Finally, the results are analysed to illustrate the proposed method performance in addition to the more traditional ABC, AHRPA, ECC, and RSA methods. The proposed work should be implemented in JAVA.

Breaking Fault Attack Countermeasures With Side-Channel Information

In the persistent fault-based collision attack (PFCA) [1], the adversary captures the information that the intermediate states have collided through identical correct/incorrect ciphertexts. However, fault countermeasures achieve suppression of incorrect ciphertexts and prevent the PFCA. In this paper, we measure the collision of internal states (or state bytes) using side-channel information. First, for round-level countermeasures, we identify state bytes hitting the same persistent fault during the first round of encryption by the shortest runtime. Additionally, we design sliding-window algorithms to automatically identify the runtime of one-round encryptions suitable for different execution environments. Second, for algorithm-level protections, we detect the collision of the internal states after the first round of encryption through the maximum similarity of power consumption traces. Meanwhile, to address the low success rate of key recovery caused by miss detection due to noise within runtime or power consumption, we further revise the original filtering algorithm in PFCA. Third, we implement round-level protected AES on PC to measure runtime, and both AES protected by round-level (or algorithm-level) countermeasures and SM4 [2] protected by a round-level countermeasure on a smart card to collect power consumption. Finally, the experimental result proves that the revised PFCA successfully recovers the key.

VECDSigL: Video integrity verification using elliptic curve digital signature links

<h2>Abstract</h2> Video footage is produced as evidence in medical investigations, legal investigations, etc. With the advancements in video editing tools, it is easy to make modifications to videos without leaving evidence of manipulations. Thus requirement of video authentication is becoming a necessity need in mobile phones, accident data recorders, Closed Circuit Television, etc. The video integrity verification system confirms that the video under consideration is not altered. We present a prototype, VECDSigL that ensures faster and more secure signature generation, blockchain storage, and integrity verification of digital videos. Video integrity verification is done using blockchain, where each block stores elliptic curve digital signatures of a video segment and the previous block. This video integrity verification system implemented is robust to different attacks such as brute force attacks and collision attacks. This system can detect forgeries of any kind and can verify the integrity of any video type.

Deep self-learning based dynamic secret key generation for novel secure and efficient hashing algorithm

The hash function is an efficient source of the integrity and authentication of input text and other data messages (image & audio-video) in the cryptography field. Existing hashing algorithms are time-consuming and vulnerable to collision attacks, birthday attacks, meet-in-the-middle attacks, and key search attacks. In this context, we propose an innovative and secure hash algorithm that uses self-learning neurons during the implementation phase. Firstly, the proposed deep learning hashing algorithm accepts the variable length (ln) of the input text and image messages (im) at the input layer 0 and divides the whole input (M) into four characters, represented as message pair (MP). If the last MP contains less than four characters, have been completed by the assumption of the special cases and processed via the OR logical operation. Subsequently, the proposed pattern matching-swapping methods have resolved the conflict of the repeated characters in each MP. Secondly, the proposed novel dynamic secret keys are generated for each MP by introducing a skip and select basic conditions by omitting the consecutive 0's or 1's in key selection. Thirdly, upon successfully processing the phases above, the proposed novel hashing algorithm generates collision-free hash values using forwarding and backward propagation. Experimental results show that our proposed hashing algorithm is efficient and significantly outperforms in terms of sensitivity generated in hash output, speed, and collision resistance compared to the existing state-of-the-art hashing algorithms.

Collusion Detection using Predictive Functions based on Android Applications

Android is used by most of the population of the users. It is an attractive target for malicious application developers due to its open source nature. These malicious writers are developing new trends to steal sensitive information from the devices. A new trend is represented as collision attack in this manner. During this attack different apps communicate via Inter-Process Communication (IPC) for variety of purposes. In this paper, a dynamic approach is proposed for automatic collision detection between communication applications. The focus of the study is on the sharing of multiple type data. Moreover, to select application for analyzing is difficult task to perform and two predictive functions has been used in this manner. The evaluation was performed on a dataset of 800 android applications for analyzing the colluding couples. The developed methodology produces an accuracy of 97.2% during the experiments by the developed system.&#x0D;

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced from the Internet of things environment, and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of things devices, resulting in the fact that the research on abstract trust mechanism cannot be directly applied to the Internet of things; On the other hand, various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered. Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals, a cross domain trust model (CDTM) based on self-authentication is proposed. Unlike most trust models, this model uses self-certified trust. The cross-domain process of internet of things (IoT) terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction. At the same time, in order to alleviate the collision attack and improve the accuracy of trust evaluation, the overall trust value is calculated by comprehensively considering the quantity weight, time attenuation weight and similarity weight. Finally, the simulation results show that CDTM has good anti collusion attack ability. The success rate of malicious interaction will not increase significantly. Compared with other models, the resource consumption of our proposed model is significantly reduced.

A modified secure hash design to circumvent collision and length extension attacks

This paper introduces an improved version of the secure hash algorithms, SHA-1, and SHA-2. The proposed work produces a strengthened secure hash design using the fusion between the SHA-1 and SHA-2 hash standards. This design helps to protect the SHA-1 and SHA-2 against collision and length extension attacks. The proposed design is incorporated in the round steps of the hash functions using the function manipulators of the SHA-1 and SHA-2. Moreover, the proposed design employs a padding method that is different from the one used in the SHA-1 and SHA-2 construction. The proposed design is verified by the official test vectors that were confirmed by the National Institute of Standard and Technology. Moreover, the avalanche effect, hamming distance, and bit-hit properties were studied using different test cases. The proposed design gives over 52 percent of the avalanche effect and over 84 hamming distance. The proposed design was tested against collision attack and produced unique hash values for real collided examples and shows resistance against length extension attack.

Finding Collisions against 4-Round SHA-3-384 in Practical Time

The Keccak sponge function family, designed by Bertoni et al. in 2007, was selected by the U.S. National Institute of Standards and Technology (NIST) in 2012 as the next generation of Secure Hash Algorithm (SHA-3). Due to its theoretical and practical importance, cryptanalysis of SHA-3 has attracted a lot of attention. Currently, the most powerful collision attack on SHA-3 is Jian Guo et al.’s linearisation technique. However, this technique is infeasible for variants with asmaller input space, such as SHA-3-384.In this work we improve upon previous results by utilising three ideas which were not used in previous works on collision attacks against SHA-3. First, we use 2-block messages instead of 1-block messages, to reduce constraints and increase flexibility in our solutions. Second, we reduce the connectivity problem into a satisfiability (SAT) problem, instead of applying the linearisation technique. Finally, we propose an efficient deduce-and-sieve algorithm on the basis of two new non-random propertiesof the Keccak non-linear layer.The resulting collision-finding algorithm on 4-round SHA-3-384 has a practical time complexity of 259.64 (and a memory complexity of 245.94). This greatly improves upon the best known collision attack so far: Dinur et al. achieved an impractical 2147 time complexity. Our attack does not threaten the security margin of the SHA-3 hash function. However, the tools developed in this paper could be used to analyse other cryptographic primitives as well as to develop new and faster SAT solvers.

Blockchain data privacy protection modeling based on CP-ABE algorithm

Abstract With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.

Chaotic neural networks and farfalle construction based parallel keyed secure hash function

AbstractParallel computing of hash functions along with the security requirements have great advantage in order to reduce the time consumption and overhead of the CPU. In this article, a keyed hash function based on farfalle construction and chaotic neural networks (CNNs) is proposed, which generates a hash value with arbitrary (defined by user) length (eg, 256 and 512 bits). The proposed hash function has parallelism merit because it is built over farfalle construction which avoids the dependency between the blocks of a given message. Moreover, the proposed hash function is chaos based (ie, it relies on chaotic maps and CNNs which have non‐periodic behavior). The security analysis shows that the proposed hash function is robust and satisfies the properties of hash algorithms, such as random‐like (non‐periodic) behavior, ideal sensitivity to original message and secret key, one‐way property and optimal diffusion effect. The speed performance of the hash function is also analyzed and compared with a hash function which was built based on sponge construction and CNN, and compared with secure hash algorithm (SHA) variants like SHA‐2 and SHA‐3. The results have shown that the proposed hash function has lower time complexity and higher throughput especially with large size messages. Additionally, the proposed hash function has enough resistance to multiple attacks, such as collision attack, birthday attack, exhaustive key search attack, preimage and second preimage attacks, and meet‐in‐the‐middle attack. These advantages make it ideal to be used as a good collision‐resistant hash function.

Developing a New Collision-Resistant Hashing Algorithm

Today, cryptographic hash functions have numerous applications in different areas. At the same time, new collision attacks have been developed recently, making some widely used algorithms like SHA-1 vulnerable and unreliable. This article is aiming at the development of a new hashing algorithm that will be resistant to all cryptographic attacks, including quantum collision attacks that potentially pose a threat to some widely used cryptographic hash functions. This algorithm was called Nik-512. The avalanche effect is tested to ensure the cryptographic strength of the developed algorithm. The Nik-512 function is then applied to build a data integrity system which can be used to protect data from malicious users.

Bit-policy based chosen-plaintext collision attack on reused-mask AES

Side-channel collision attacks have shown good efficiency on breaking unprotected or reused-mask implementations of block ciphers. Depending on the relationship between “Hamming distance of values” and “average distance of traces”, this work presents a novel bit-policy based chosen-plaintext collision attack to break a software implementation of reused-mask AES. Some factors affecting the success rates of the proposed scheme and their reasons are analyzed, such as how to measure the distance of traces, signal-to-noise ratio (SNR), how to select points of interest (POI) and the number of traces (n). In this paper, two measurements, the least square method (LSM) and the central moment product method (CMP), are adopted to calculate the distance of traces. Then their probability distribution and efficiency are theoretically analyzed and simulated. As a result, CMP outperforms LSM, and all the other three factors, i.e. SNR, POI, n, are relevant to the distribution of LSM and CMP. When LSM and CMP are combined with the proposed scheme and the compared ones, both theoretical and practical analysis are given to illustrate how the number of traces impacts on the success rates at different levels of SNR; as a result the developed attack has the best performance among all the compared schemes at both low and high level of SNR, especially at high level of SNR. To gain a success rate of 95%, at SNR of 0 dB and −8 dB, our optimal scheme and the compared optimal scheme have 800 and 4500 less energy traces, respectively. Furthermore, our attack requires no detailed knowledge about the design of SBoxes, nor does it require templates for profiling.

Multi‐channel attack detection based on lightweight message authentication code access control using Internet of Things design

AbstractThe Internet of Things (IoT) is a network of interconnected, Internet‐connected items that can gather and transmit data without the need for human interaction through a wireless network. Cryptography is a way of using codes to safeguard information and communications so that only those who are supposed to read and process it may do so. Existing system shows privacy and replication issues caused during cipher text attribute set based encryption. The technical issues in achieving secured authentication is removed by using multi‐channel attack detection based on lightweight message authentication code (LMAC) access control using IoT design in which collision attack can be removed by using secure hash algorithm based Merkle tree and to control sinkhole attacks agent navigation algorithm is proposed and long short‐term memory (LSTM) recurrent neural network (RNN) is proposed to provide the conformity of the user login password. Moreover, to identify attackers on the transmitter side by ensuring authentication, multichannel detection method is proposed. Finally, the security and privacy is improved by using LMAC and user revocation. Thus the multi‐channel attack detection based on LMAC using IoT design can be outperforming the other existing model with higher throughput and low execution time.

A novel model to enhance the data security in cloud environment

Nowadays cloud computing has given a new paradigm of computing. Despite several benefits of cloud computing there is still a big challenge of ensuring confidentiality and integrity for sensitive information on the cloud. Therefore to address these challenges without loss of any sensitive information and privacy, we present a novel and robust model called ‘Enhanced Cloud Security using Hyper Elliptic Curve and Biometric’ (ECSHB). The model ECSHB ensures the preservation of data security, privacy, and authentication of data in a cloud environment. The proposed approach combines biometric and hyperelliptic curve cryptography (HECC) techniques to elevate the security of data accessing and resource preservations in the cloud. ECSHB provides a high level of security using less processing power, which will automatically reduce the overall cost. The efficacy of the ECSHB has been evaluated in the form of recognition rate, biometric similarity score, False Matching Ratio (FMR), and False NonMatching Ratio (FNMR). ECSHB has been validated using security threat model analysis in terms of confidentiality. The measure of collision attack, replay attack and non-repudiation is also considered in this work. The evidence of results is compared with some existing work, and the results obtained exhibit better performance in terms of data security and privacy in the cloud environment.

FPGA Implementation of Secure Block Creation Algorithm for Blockchain Technology

Blockchain technology is essential to secure storage, authenticate data, and protect information from being misused and exploitation. Traditional methods of securing data using cryptographic algorithms include hashing functions like SHA-0, SHA-1, which have limitations like excess computational time, collision attacks, scalability, backtracking to retrieve the original message, etc. Using a combination of RSA and SHA together allows us to create a block on an FPGA, which when combined with other blocks establishes an encrypted Blockchain, which overcomes such limitations. Synthesis and implementation of the encrypted block have been compared and analyzed on Virtex-4, Virtex-5, and Spartan-6 FPGA boards. Based on the resource requirement like the slice registers, LUT-FF pairs and memory, Virtex-5 was chosen. Complete security is achieved as the hashing process is irreversible and backtracking of data is not possible. Previous problems of strengthening security, backtracking, excessive memory usage, and zero collision attacks are addressed and solved.

Higher-Order Masked Ciphertext Comparison for Lattice-Based Cryptography

Checking the equality of two arrays is a crucial building block of the Fujisaki-Okamoto transformation, and as such it is used in several post-quantum key encapsulation mechanisms including Kyber and Saber. While this comparison operation is easy to perform in a black box setting, it is hard to efficiently protect against side-channel attacks. For instance, the hash-based method by Oder et al. is limited to first-order masking, a higher-order method by Bache et al. was shown to be flawed, and a very recent higher-order technique by Bos et al. suffers in runtime. In this paper, we first demonstrate that the hash-based approach, and likely many similar first-order techniques, succumb to a relatively simple side-channel collision attack. We can successfully recover a Kyber512 key using just 6000 traces. While this does not break the security claims, it does show the need for efficient higher-order methods. We then present a new higher-order masked comparison algorithm based on the (insecure) higher-order method of Bache et al. Our new method is 4.2x, resp. 7.5x, faster than the method of Bos et al. for a 2nd, resp. 3rd, -order masking on the ARM Cortex-M4, and unlike the method of Bache et al., the new technique takes ciphertext compression into account. We prove correctness, security, and masking security in detail and provide performance numbers for 2nd and 3rd-order implementations. Finally, we verify our the side-channel security of our implementation using the test vector leakage assessment (TVLA) methodology.

Investigating the Avalanche Effect of Various Cryptographically Secure Hash Functions and Hash-Based Applications

In modern cryptography, hash functions are considered as one of the key components for secure communication. They play a vital role in a wide range of applications such as ensuring the authentication and integrity of the data, in forensic investigation, password storage, random number generations for unique session keys, and for creating a unified view in blockchain. The Avalanche effect (also known as diffusion) is an important characteristic of a hash function where a minor change in the hash function’s input will result in a significantly different output. The absence of this property implies that the hash function is vulnerable to various attacks such as collision attack, length extension attack, and preimage attack. Through this research, we have investigated the Avalanche effect of sixteen hash functions and two hash-based applications, namely Hash-based Message Authentication Code (HMAC) and Public Key Cryptography Standards (PKCS). To measure the performance of these hash functions and hash-based applications, we have implemented a generic circuit using CrypTool for automating the simulation process for multiple trials. Simulation results indicate that around half of the inputs of each hash function failed to exhibit the Strict Avalanche Criterion (SAC) and, Bit Independence Criterion (BIC). Moreover, we ranked the hash functions based on Multi Criteria Decision Metrics (MCDM) using intermediate states of simulation results. Furthermore, a total of fifteen statistical tests were carried out to evaluate the randomization property of the hash functions using NIST (National Institute of Standards and Technology) toolkit. This study is aimed to open up a future scope of research to the need for improvement of various hash functions by analyzing the randomization and non-correlation properties of existing functions in terms of the Avalanche effect.