Ciphertext-only Attack Research Articles

Privacy-Preserving ConvMixer Without Any Accuracy Degradation Using Compressible Encrypted Images

We propose an enhanced privacy-preserving method for image classification using ConvMixer, which is an extremely simple model that is similar in spirit to the Vision Transformer (ViT). Most privacy-preserving methods using encrypted images cause the performance of models to degrade due to the influence of encryption, but a state-of-the-art method was demonstrated to have the same classification accuracy as that of models without any encryption under the use of ViT. However, the method, in which a common secret key is assigned to each patch, is not robust enough against ciphertext-only attacks (COAs) including jigsaw puzzle solver attacks if compressible encrypted images are used. In addition, ConvMixer is less robust than ViT because there is no position embedding. To overcome this issue, we propose a novel block-wise encryption method that allows us to assign an independent key to each patch to enhance robustness against attacks. In experiments, the effectiveness of the method is verified in terms of image classification accuracy and robustness, and it is compared with conventional privacy-preserving methods using image encryption.

Verifiable Privacy-Enhanced Rotation Invariant LBP Feature Extraction in Fog Computing

Rotation invariant local binary pattern (RI-LBP) features have been applied in diverse scenarios with the advantages of gray-scale and rotation invariance. Secure fog computing has become an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for feature extraction. Prior secure outsourcing protocols based on LBP and RI-LBP simply focus on local data privacy, which can only resist ciphertext-only attack, and also make extracted features exposed to the cloud. This work focuses on how to effectively ensure data confidentiality and feature integrity. We propose a verifiable privacy-enhanced protocol for RI-LBP feature extraction (VRLBP) based on the fog computing paradigm, which mitigates the aforementioned challenges by involving the proposed symmetric cryptographic scheme where local data and extracted features are proven secure against chosen plaintext attack. Meanwhile, the stage of verification can check the correctness of outsourced features with an overwhelming probability and constant computational complexity. The security analysis and computational costs demonstrate that VRLBP can reduce the computation overhead to around 30% of original feature extraction in a privacy-preserving manner. To exhibit the practical utility, VRLBP is implemented for deepfake detection on five public datasets. Extensive evaluations indicate that VRLBP achieves almost the same accuracy as the original RI-LBP algorithm and outperforms the state-of-the-art protocols.

Modifikasi algoritma Hill cipher dengan matriks kunci berupa matriks ketetanggaan

Cryptography is one of the parts in mathematics, especially applied mathematics. Cryptography is the science that studies how to secure information that we don't want others to know about the information we have. Based on the development of the times, cryptography is divided into two consisting of classical cryptography and modern cryptography. In this paper, the focus of discussion is on classical cryptography. Furthermore, there are many kinds of classical cryptographic algorithms, such as the Caesar cipher algorithm, the Playfair cipher algorithm, the Vigenere cipher algorithm, and the Hill Cipher algorithm. The focus of discussion in this paper is on the Hill cipher algorithm. The modifications made to the Hill cipher algorithm lie in its key matrix. In this paper, it will be shown how to encrypt and decrypt the message we want to send using the Hill cipher algorithm with the matrix being a adjacency matrix. In which case, the adjacency matrix itself is obtained from the representation of the graph. It is hoped that this algorithm can avoid crypt attacks, especially on ciphertext only-attack.Keywords: Hill cipher algorithm, cryptography, adjacency matrix, plaintext, ciphertextMSC2020: 94A60

Frequency-Revealing Attacks against Frequency-Hiding Order-Preserving Encryption

Order-preserving encryption (OPE) allows efficient comparison operations over encrypted data and thus is popular in encrypted databases. However, most existing OPE schemes are vulnerable to inference attacks as they leak plaintext frequency. To this end, some frequency-hiding order-preserving encryption (FH-OPE) schemes are proposed and claim to prevent the leakage of frequency. FH-OPE schemes are considered an important step towards mitigating inference attacks. Unfortunately, there are still vulnerabilities in all existing FH-OPE schemes. In this work, we revisit the security of all existing FH-OPE schemes. We are the first to demonstrate that plaintext frequency hidden by them is recoverable. We present three ciphertext-only attacks named frequency-revealing attacks to recover plaintext frequency. We evaluate our attacks in three real-world datasets. They recover over 90% of plaintext frequency hidden by any existing FH-OPE scheme. With frequency revealed, we also show the potentiality to apply inference attacks on existing FH-OPE schemes. Our findings highlight the limitations of current FH-OPE schemes. Our attacks demonstrate that achieving frequency-hiding requires addressing the leakages of both non-uniform ciphertext distribution and insertion orders of ciphertexts, even though the leakage of insertion orders is always ignored in OPE.

A Jigsaw Puzzle Solver-Based Attack on Image Encryption Using Vision Transformer for Privacy-Preserving DNNs

In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using the Vision Transformer (ViT) is known to be robust against existing COAs due to the operations of block scrambling and pixel shuffling, which permute divided blocks and pixels in an encrypted image. However, the correlation between blocks in the encrypted image can still be exploited for reconstruction. Therefore, in this paper, a novel jigsaw puzzle solver-based attack that utilizes block correlation is proposed to restore visual information from encrypted images. In the experiments, we evaluated the security of image encryption for privacy-preserving deep neural networks using both conventional and proposed COAs. The experimental results demonstrate that the proposed attack is able to restore almost all visual information from images encrypted for being applied to ViTs.

Deep-learning-based cross-talk free and high-security compressive encryption with spatially incoherent illumination.

Incoherent optical cryptosystem is promising for its immunity against coherent noise and insensitivity to misalignment, and compressive encryption is desirable considering the increasingly demand on the exchange of encrypted data via Internet. In this paper, we propose a novel optical compressive encryption approach with spatially incoherent illumination based on deep learning (DL) and space multiplexing. For encryption, the plaintexts are individually sent to the scattering-imaging-based encryption (SIBE) scheme where they are transformed to scattering images with noise appearances. Afterwards, these images are randomly sampled and then integrated into a single package (i.e., ciphertext) by space multiplexing. The decryption is basically the inverse of the encryption, while it involves an ill-posed problem (i.e., recovering the noise-like scattering image from its randomly sampled version). We demonstrated that such a problem can be well resolved by DL. The proposal is radically free from the cross-talk noise existing in many current multiple-image encryption schemes. Also, it gets rid of the linearity bothering the SIBE and is hence robust against the ciphertext-only attack based on phase retrieval algorithm. We present a series of experimental results to confirm the effectiveness and feasibility of the proposal.

CS-LeCT: Chained Secure and Low-Energy Consumption Data Transmission Based on Compressive Sensing

Energy consumption and security are two major challenges faced by telemedicine systems. Due to the simplicity and the randomness in information acquisition process, compressive sensing (CS) has a vast prospect in dealing with these two problems. Recently, a chained CS (CCS) scheme has been proposed, which can implement a secure and low-energy consumption data transmission. However, due to its chain computation and the lossy compression nature of CS, there exists an accumulative error in the existing CCS scheme. Moreover, the block-based CS decoding also leads to a significant performance deterioration. In this paper, a new chained secure and low-energy consumption data transmission (CS-LeCT) scheme has been proposed, whose reconstruction performance is far superior to the CCS. Firstly, to overcome the accumulative error of the CCS scheme, square matrices instead of under-determined matrices were used to encode the signal so that in the proposed CS-LeCT the decoding process can be carried out by adopting a lossless inverse operation. Secondly, inspired by the structurally random matrix (SRM), an active packet drop strategy was designed to achieve low-energy consumption data transmission, whose decoding can be implemented by means of the SRM-based CS reconstruction. The performance gain of the CS-LeCT has not only been validated through simulation experiments but also been analyzed theoretically. Safety analysis also shows that the proposed CS-LeCT can resist several potential attacks, such as ciphertext-only attacks (COA), known-plaintext attacks (KPA) and man-in-the-middle attacks (MiTM).

Statistical Differential Fault Analysis of the Saturnin Lightweight Cryptosystem in the Mobile Wireless Sensor Networks

Mobile wireless sensor networks (MWSNs) have blended into a new application scenario to create revolutionary intelligent cities and handle numerous challenges of security, adaptability, and robustness as wireless technology has advanced. Exploiting lightweight cryptosystems is considered one of the main approaches for MWSNs to achieve confidentiality, integrity, and authentication to prevent malicious cyberattacks and resource abuses. The Saturnin lightweight cryptosystem, presented at ToSC in 2020, can be applied to protect MWSNs. No literature suggests Saturnin can defend against a ciphertext-only attack, where the attackers are the most susceptible. This study proposes the novel statistical differential fault analysis (SDFA) in this attack scenario with double distinguishers of square Chi-maximum likelihood estimate and Dice-Hamming weight. Following the experiments, it recovered the 256-bit secret key using 656 faults in the fourth-to-last round of Saturnin. Compared to the classic statistical fault analysis (SFA), the novel SDFA can expand fault injections to the deeper round and decrease the faults by half with a reliability of at least 99%. It gives an essential reference for figuring out how secure lightweight cryptosystems in MWSNs are.

High-performance reversible data hiding in encrypted images with adaptive Huffman code

With the development of cloud computing, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention. In this paper, a high-performance RDHEI algorithm with Adaptive Huffman Code (RDHEI-AHC) is proposed for cloud computing. First, multi-MSB (Most Significant Bit) of each pixel is predicted by geometric correction, which takes full advantage of spatial correlation. Next, the image is encrypted by stream cipher. Adaptive Huffman code is then used to insert the auxiliary information and secret message into the encrypted image. Finally, to resist ciphertext-only attacks, the marked encrypted image must be scrambled. In comparison to most state-of-the-art RDHEI approaches, experimental results show that the RDHEI-AHC method delivers a greater embedding rate and improved security.

Security analysis on optical cryptosystem based on interference and phase-retrieval technique

In this paper, the security strength of an optical cryptosystem based on interference and phase-retrieval technique has been evaluated. Compared to the conventional interference-based scheme in which private keys are generated according to the principle of vector decomposition, a modified error-reduction phase retrieval algorithm is employed to produce a plaintext-dependent key with the smaller size than that of the plaintext in the improved cryptosystem; thus, resulting in higher resistance against iterative attacks and lower burden of data transmission. However, it is found that only one phase-only mask (POM) served as the private key is unknown to illegal users, the key space is limited; thus, it is possible to investigate an iterative process to recover the unknown key with the aid of public keys and the arbitrarily given ciphertext as additional constraints. And employing the recovered private key, encoded information may be reconstructed. Based on this cryptoanalysis, the iterative ciphertext-only attack (COA) with normalization and image smoothing techniques (i.e., the median filter, the bilateral filter) is proposed to crack the enhanced scheme. Numerical simulations are carried out to validate the feasibility and effectiveness of the proposed COA.

Deep-learning-based ciphertext-only attack on optical scanning cryptosystem

Optical scanning cryptography (OSC) has garnered considerable attention because of its ability to acquire an incoherent hologram from a physical object using a single-pixel sensor. Security analysis is crucial to improve the performance of cryptographic systems. In this study, we prove that the optical scanning cryptosystem is vulnerable to a deep-learning-based ciphertext-only attack (COA) strategy. The conventional algorithm denotes the decryption process as an inverse problem of the encryption process and directly trains the deep learning architecture with a large number of pairs of plaintext-ciphertext images. However, this study first presents a combined deep-learning-based framework dedicated to processing COA on OSC using three deep neural networks (DNNs). With these DNNs, the plaintext image can be retrieved from an unknown ciphertext image in real time. The proposed method is independent of plaintext-ciphertext image pairs acquired from the OSC system, and thus makes COA available. Further, the proposed deep-learning-based COA method can attack an optical scanning cryptosystem encrypted with either pure phase keys or complex amplitude keys. Simulations and experimental results proved the feasibility and effectiveness of the proposed deep-learning-based COA method.

Privacy-Preserving Threshold-Based Image Retrieval in Cloud-Assisted Internet of Things

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> ranked image retrieval solutions have low retrieval efficiency and may leak the values and orders of similarity scores to the cloud server. Hence, if a malicious server learns user background information through some improper means, then the malicious server can potentially infer user preferences and guess the most similar image content according to similarity scores. To solve the above challenges, we propose a privacy-preserving threshold-based image retrieval scheme using the convolutional neural network (CNN) model and a secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (kNN) algorithm, which improves the retrieval efficiency and prevents the cloud server from learning the values and orders of similarity scores. Formal security analysis shows that our proposed scheme can resist both ciphertext-only attack (COA) and chosen-plaintext attack (CPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible for real-world data sets.

Security analysis on the interference-based optical image cryptosystem with a designed amplitude modulator.

The security strength of an interference-based optical image cryptosystem with a designed amplitude modulator (AM) is evaluated. Compared to previous improved interference-based cryptosystems in which iterative or post-processing algorithms are involved, to remove the silhouette problem, a pre-designed AM is placed at the image plane to modulate the plaintext directly, which eliminates this drawback. Moreover, the AM is used as an additional private key, which enlarges the key space and further enhances the security system. However, we have noticed that one of the designed parameters in the AM has a low key sensitivity, which is fault-tolerant and is not required for precise reconstruction. Additionally, the AM is plaintext-independent, which means that the AM can be recovered first by using a pair of chosen plaintexts and the corresponding ciphertext. Based on these findings, we propose a hybrid algorithm that includes two chosen-plaintext attacks (CPAs) and a ciphertext-only attack (COA) to break the enhanced cryptosystem. Specially, CPAs with an impulse function and an arbitrarily given plaintext are proposed to retrieve two parameters in the AM. With the retrieved AM, information on the plaintext can be recovered from the corresponding ciphertext by two kinds of COAs without any knowledge of the private keys. To the best of our knowledge, this is the first time that the interference-based cryptosystem with a designed AM has been cracked successfully, which may provide potential evidence for further security improvements. Numerical simulations are carried out to validate the feasibility of the proposed attacks.

A novel cryptosystem based on DNA cryptography, hyperchaotic systems and a randomly generated Moore machine for cyber physical systems

Nowadays, a large amount of data is transmitted over the Internet and in Cyber–Physical Systems (CPS). A significant portion of this data is highly confidential. Bank account details, credit card details, One Time Passwords (OTP), financial data and other sensitive information are some examples of highly confidential data. A secure encryption and decryption technique is needed to ensure the secure transmission of data. These techniques use a secret key and guarantee that confidential data is only accessible to authorized individuals. This paper proposes a novel encryption process based on Deoxyribonucleic Acid (DNA) cryptography, a hyperchaotic system and a Moore machine. The hyperchaotic system generates four pseudo-random number sequences used in DNA-based operations. The Moore machine performs substitutions in the DNA sequence that makes the system more secure. The proposed technique can protect a system from various attacks, namely man-in-the-middle attacks, ciphertext-only attacks, known-plaintext attacks, brute force attacks and differential cryptanalysis attacks. The proposed scheme gives an average avalanche effect of 54.75%, which guarantees a high level of robustness. Moreover, experimental results show that the proposed scheme is more secure and efficient than the existing schemes.

Improved Ciphertext-Only Attack on GMR-1

The GEO-Mobile Radio Interface-1 (GMR-1) is a satellite communication standard used in Thuraya, a United Arab Emirates-based regional mobile satellite service provider. The specification of the encryption algorithm used in GMR-1 was not disclosed until it was uncovered by Driessen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> in 2012 through reverse engineering. Given that A5-GMR-1, a stream cipher used in GMR-1, is primarily based on A5/2, Driessen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> presented a ciphertext-only attack from the attacks on A5/2. Their ciphertext-only attack recovers the session key from multiple sets of 24 ciphertexts in an average of 32.1 min and requires 400 GB of pre-computed data. This study enhances Driessen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s ciphertext-only attack on A5-GMR-1 in all aspects of time, memory, and data. Our contributions are fourfold. First, we optimize the inefficient part of the previous attack. As a result, our ciphertext-only attack recovers the session key from multiple sets of 13 ciphertexts in less than 1 second and requires 400 MB of pre-computed data. Second, we propose novel memory-saving techniques. These techniques reduce the memory complexity to 216 ~ 289 MB without increasing the time and data complexity. Third, we present several time-memory-data tradeoff techniques. Using these techniques, we can present an attack that meets the desired conditions, such as memory minimization or data minimization. Furthermore, while the complexity of the previous attack is presented vaguely as “multiple sets” of 24 ciphertexts, these techniques allow us to accurately calculate the time, memory, and data complexity of the attack. Finally, we demonstrate that A5-GMR-1 can be attacked without frame numbers. To find out the frame number of each ciphertext, it is necessary to analyze and synchronize multiple channels. We present a plaintext recovery attack that does not require these processes.

Amino Acid Encryption Method Using Genetic Algorithm for Key Generation

In this new information era, the transfer of data and information has become a very important matter. Transferred data must be kept secured from unauthorized persons using cryptography. The science of cryptography depends not only on complex mathematical models but also on encryption keys. Amino acid encryption is a promising model for data security. In this paper, we propose an amino acid encryption model with two encryption keys. The first key is generated randomly using the genetic algorithm. The second key is called the protein key which is generated from converting DNA to a protein message. Then, the protein message and the first key are used in the modified Playfair matrix to generate the cypher message. The experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only attack. In addition, the proposed model has been tested over different types of characters including white spaces and special characters, as all the data is encoded to 8-bit binary. The performance of the proposed model is compared with other models using encryption time and decryption time. The model also balances all three principles in the CIA triad.

Design Prototype and Security Analysis of a Lightweight Joint Compression and Encryption Scheme for Resource-Constrained IoT Devices

Compressive sensing (CS) can provide joint compression and encryption, which is promising to address the challenges of massive sensor data and data security in the Internet of Things (IoT). However, as IoT devices have constrained memory, computing power, and energy, in practice the CS-based computationally secure scheme is shown to be vulnerable to ciphertext-only attack for short-signal length. Although the CS-based perfectly secure scheme has no such vulnerabilities, its practical realization is challenging. In this article, we propose an energy concealment (EC) encryption scheme, a practical realization of the perfectly secure scheme by concealing energy, thereby removing the requirement of an additional secure channel. We propose three different methods to generate sensing matrix to improve energy efficiency using linear feedback shift registers and lagged Fibonacci sequences. Leveraging the signal’s maximum energy in the EC scheme, we design a new measure to evaluate reconstructed signal quality without the knowledge of the original signal. Furthermore, a new CS decoding algorithm is designed by incorporating the knowledge of maximum energy at the decoder, which improves the signal reconstruction quality while reducing the number of measurements. Additionally, our comprehensive security analysis shows that the EC scheme is secure against various cryptographic attacks. We implement the EC scheme using the three different ways of generating the sensing matrix in the resource-constrained TelosB mote using the Contiki operating system. The experimental results demonstrate that the EC scheme outperforms advanced encryption standard in terms of code memory footprint and total energy consumption.

Analysis of a late 19th century french cipher created by Major Josse

Rémi Géraud-Stewart and David Naccache have recently discovered historical documents from the late 19th century, written by Major H.D. Josse of the French Army, that describe a proposal for a new cipher. In our paper, we provide an in-depth analysis of the cipher and a revised formal description of the cipher based on the examples given by Josse. We also present classes of cryptologically equivalent keys, and a possible attack based on ciphertext isomorphs, that can be implemented with only pen-and-paper. We also describe a new ciphertext-only stochastic attack, based on simulated annealing, that can recover the key and the plaintext from ciphertexts with only 75 letters. We are planning to implement the cipher in CrypTool 2, an e-learning platform for cryptography.

Cryptographic analysis on an optical random-phase-encoding cryptosystem for complex targets based on physics-informed learning.

Optical cryptanalysis based on deep learning (DL) has grabbed more and more attention. However, most DL methods are purely data-driven methods, lacking relevant physical priors, resulting in generalization capabilities restrained and limiting practical applications. In this paper, we demonstrate that the double-random phase encoding (DRPE)-based optical cryptosystems are susceptible to preprocessing ciphertext-only attack (pCOA) based on DL strategies, which can achieve high prediction fidelity for complex targets by using only one random phase mask (RPM) for training. After preprocessing the ciphertext information to procure substantial intrinsic information, the physical knowledge DL method based on physical priors is exploited to further learn the statistical invariants in different ciphertexts. As a result, the generalization ability has been significantly improved by increasing the number of training RPMs. This method also breaks the image size limitation of the traditional COA method. Optical experiments demonstrate the feasibility and the effectiveness of the proposed learning-based pCOA method.

Ciphertext-only attacks on the double random phase encryption based on redundancy vulnerability

The double random phase encoding techniques have received considerable attention from researchers in recent years because of their advantages of parallel and high-speed processing capability. Meanwhile, the security of the cryptosystem is also one of the major concerns. We experimentally showed the ciphertext redundancy vulnerability of the coherent double random phase encryption (DRPE) system. Based on the statistical ergodicity of speckles and the consistency of the energy spectral density (ESD), we have proved that the method can retrieve the most plaintext information from partial ciphertext alone. We performed the simulation and experimental results to verify whether the algorithm is effective. The ciphertext redundancy of the DRPE system is analyzed from the results of the ciphertext occlusion test. There is a risk of plaintext leakage in this scheme, as long as the average ESD can be estimated from the sub-images. The results will further our understanding of the limitation of current optical security techniques. The DRPE system has potential redundancy risk. This vulnerability allows a cryptanalyst to estimate the plaintext information with only a half or less ciphertext.