Frequency Analysis Attack Research Articles

A randomized encryption deduplication method against frequency attack

Encryption deduplication technology first encrypts the file or chunks of data, then detects and removes duplicate ciphertexts, storing only one copy on the cloud storage server (CSP). Although traditional encryption deduplication schemes have the function of ciphertext deduplication, they also have a serious problem. Since the key generation algorithm and the encryption algorithm are deterministic, the frequency of plaintext chunking will be leaked. It is possible for the attacker to infer the information of the plaintext. We propose a Randomized Encryption deduplication method against Frequency Attack (REFA) that provides high randomness in ciphertexts. The core idea of REFA is that after obtaining a convergence key generated with the aid of a key server, the user randomizes the convergence key with a random value of the same length. Then, REFA encrypts the plaintext chunk with a randomized key. Our scheme hides the random value in the ciphertext and encrypts the convergence key for user ownership verification. REFA perfectly masks the frequency of plaintext chunks and has high storage efficiency and data security. Furthermore, the CSP performs ciphertext updates with ciphertexts uploaded by subsequent users. REFA can effectively protect against frequency analysis attacks.

Improving Performance of Searchable Symmetric Encryption Through New Information Retrieval Scheme

Searchable symmetric encryption (SSE) is a robust cryptographic method that allows users to store and retrieve encrypted data on a remote server, such as a cloud server, while maintaining the privacy of the user's data. The technique employs symmetric encryption, which utilizes a single secret key for both data encryption and decryption. However, extensive research in this field has revealed that SSE encounters performance issues when dealing with large databases. Upon further investigation, it has become apparent that the issue is due to poor locality, necessitating that the cloud server access multiple memory locations for a single query. Additionally, prior endeavors in this domain centered on locality optimization have often led to expanded storage requirements (the stored encrypted index should not be substantially larger than the original index) or diminished data retrieval efficiency (only required data should be retrieved). We present a simple, secure, searchable, and cost-effective scheme, which addresses the aforementioned problems while achieving a significant improvement in information retrieval performance through site optimization by changing the encrypted inverted index storage mechanism. The proposed scheme has the optimal locality O(1) and the best read efficiency O(1) with no significant negative impact on the storage space, which often increases due to the improvement of the locality. Using real-world data, we demonstrate that our scheme is secure, practical, and highly accurate. Furthermore, our proposed work can resist well-known attacks such as keyword guessing attacks and frequency analysis attacks.

Public key authenticated searchable encryption against frequency analysis attacks

To retrieve data in form of encryption, Boneh et al. proposed a primitive called the public key encryption with keyword search (PEKS). Unfortunately, PEKS is inherently vulnerable to the keyword guessing attack (KGA). To resist such an attack, Huang et al. introduced an authenticated version of PEKS (i.e., PAEKS), in which the sender not only encrypts the keyword, but also uses his/her secret key to authenticate it. Since then, many PAEKS schemes have emerged to improve the security model of the PAEKS system.However, none of these PAEKS schemes consider frequency analysis attacks (FAA). This attack works by tabulating the frequency distribution of trapdoors generated by user when searching, i.e., placing trapdoors embedded with the same keyword in the same category and recording the number of trapdoors in each category. These frequencies are then compared with the priori knowledge of the frequency of keyword searched by user to extract the keyword embedded in the trapdoor, thereby corrupting the user's search privacy.This work formally analyzes how frequency analysis attacks lead to the leakage of keyword information of the trapdoor in the PAEKS system. Then, to resist against this attack, we propose the first PAEKS scheme with frequency analysis attack resistance to protect the user's search privacy. In order to simplify the management of the sender/receiver's public keys, we further propose our second PAEKS scheme based on the first scheme. Finally, we show that both of these two schemes are proven secure under static assumptions.

Privacy-Preserving Deduplication of Sensor Compressed Data in Distributed Fog Computing

Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.

Modified Playfair cryptosystem for improved data security

Playfair is the earliest known classical block cipher which is capable of taking two characters as a unit in the process of encryption and decryption. However, the cipher is suffering from vulnerability to many cryptanalysis attacks due to a lack of confusion and diffusion properties, an inability to handle numbers and special characters in the process of encryption and decryption, and a host of other deficiencies. Although several modifications and improvements had been done by different researchers, the emphasis has been on the modification of the key matrix to accommodate more characters to increase the keyspace. No attention has been given to increment in the size of the block that the Playfair cipher can handle at a time. In this paper, a modified Playfair (MPF) cryptosystem that is capable of handling different block sizes with high diffusion and confusion properties is developed. cryptanalysis of the developed cryptosystem was carried out and the results show that the MPF cryptosystem is resistant to Known plaintext attack, chosen-plaintext attack, chosen ciphertext attack, frequency analysis attack, autocorrelation attack, differential cryptanalysis attacks, entropy attacks, brute force attack, and can handle variable block sizes.

Modified Playfair cryptosystem for improved data security

Playfair is the earliest known classical block cipher which is capable of taking two characters as a unit in the process of encryption and decryption. However, the cipher is suffering from vulnerability to many cryptanalysis attacks due to a lack of confusion and diffusion properties, an inability to handle numbers and special characters in the process of encryption and decryption, and a host of other deficiencies. Although several modifications and improvements had been done by different researchers, the emphasis has been on the modification of the key matrix to accommodate more characters to increase the keyspace. No attention has been given to increment in the size of the block that the Playfair cipher can handle at a time. In this paper, a modified Playfair (MPF) cryptosystem that is capable of handling different block sizes with high diffusion and confusion properties is developed. cryptanalysis of the developed cryptosystem was carried out and the results show that the MPF cryptosystem is resistant to Known plaintext attack, chosen-plaintext attack, chosen ciphertext attack, frequency analysis attack, autocorrelation attack, differential cryptanalysis attacks, entropy attacks, brute force attack, and can handle variable block sizes.

University of Library Studies and Information Technologies

This paper provides implementations of three well-known ciphers – Caesar Cipher, Vigenere Cipher and Hill Cipher in Microsoft Excel. It is shown how the ciphers can be broken by using Brute-force Attack, Frequency Analysis Attack and Known-plaintext Attack. For the purpose of the cryptanalysis the relative occurrence frequencies of the letters and the index of coincidence (κ ) in Bulgarian language are determined. The classical Frequency Analysis Attack is modified using the cross-correlation between frequencies of letters in the natural language and the cipher text. Modular matrix operations in MS Excel are shown.

Security-Aware and Efficient Data Deduplication for Edge-Assisted Cloud Storage Systems

Data deduplication at the network edge significantly improves communication efficiency in edge-assisted cloud storage systems. With the increasing concern about data privacy, secure deduplication has been proposed to provide data security while supporting deduplication. Since conventional secure deduplication schemes are mainly based on deterministic encryption, they are vulnerable to frequency analysis attacks. Some recent research has focused on this problem, where several works studied the trade-off between deduplication efficiency and resistance to frequency analysis attacks. However, no existing work can provide different deduplication efficiency and protection for different data chunks. In this paper, we propose a security-aware and efficient data deduplication scheme for edge-assisted cloud storage systems. It not only improves the efficiency of deduplication but also reduces information leakage caused by frequency analysis attacks. In particular, we first define the security level for chunks to measure the security needs of users. Then, we develop an encryption scheme with multiple levels of security for deduplication. It provides higher security protection for chunks with higher security levels, while sacrificing security to achieve higher deduplication efficiency for chunks with lower security levels. We also analyze the security of our proposed scheme. Evaluations on the real-world datasets show the efficiency of our design.

Efficient privacy-preserving frequent itemset query over semantically secure encrypted cloud database

In recent years, more users tend to use data mining as a service (DMaaS) provided by cloud service providers. However, while enjoying the convenient pay-per-use mode and powerful capacity of cloud computing, users are also threatened by the potential risk of privacy leakage. In this paper, we aim to efficiently perform privacy-preserving DMaaS, and focus on frequent itemset mining over encrypted database in outsourced cloud environment. Existing work apply different encryption methods to design various privacy-preserving mining solutions. Nevertheless, these approaches either cannot provide sufficient security requirements, or introduce heavy computation costs. Some of them also need users staying on-line to execute computations, which are not practical in real-world applications. In this paper, we propose a novel efficient privacy-preserving frequent itemset query (PPFIQ) scheme using two homomorphic encryptions and ciphertext packing technique. The proposed scheme protects transaction database with semantic security, preserves mining privacy and resists frequency analysis attacks. Meanwhile, efficiency is guaranteed by inherent parallel computations for packed plaintexts and users could stay off-line during the mining process. We provide formal security analysis and evaluate the performance of our scheme with extensive experiments. The experiment results demonstrate that the proposed scheme can be efficiently implemented on large databases.

Toward fault-tolerant and secure frequent itemset mining outsourcing in hybrid cloud environment

Due to the rising costs of maintaining IT infrastructures for large-scale data mining, it is becoming a trend for data owners to outsource data mining tasks together with storage to cloud service providers, however, which also arouses security concerns on unauthorized breaches of data confidentiality and result integrity. Existing solutions yet seldom protect data privacy whilst guaranteeing result integrity. To address these issues, this paper proposes a series of privacy-preserving building blocks by employing Shamir’s secret sharing scheme. Based on those subprotocols, an efficient frequent itemset mining protocol is designed under hybrid cloud setting, in which the public unreliable cloud and semi-trusted cloud cooperate to mine frequent patterns over the encrypted database. Our scheme not only protects the privacy of datasets from frequency analysis attack, but also verifies the integrity of mining results. Theoretical analysis demonstrates that the scheme ensures security as well as fault tolerance under our threat model. Experimental evaluations show that our proposed protocol outperforms the similar solution regarding efficiency while it can detect and correct cloud servers’ errors effectively.

Securing the IOT Devices with Artificial Immune System

Security is the main concern for IOT devices as are expected to share a lot of crucial information about the user and his surroundings. The traditional security mechanisms are ineffective against sophisticated and advanced security attacks such as Man in the Middle Attack, Denial of Service attack, Identity cloning. Different solutions have been proposed for user authentication. Device authentication is crucial in IOT environment and cannot be neglected. Despite this device authentication has not gained equal attention from the research community. The aim of this research is to develop a lightweight and robust device authentication algorithm by Artificial Immune System to ensure data integrity in IoT networks. The concepts of Artificial Immune system are utilized for generating a non-redundant device signature which is used to differentiate between authentic and malicious nodes. The device signature is generated dynamically and is non reusable. This property makes the proposed algorithm secure against numerous high-level attacks such as frequency analysis attacks, Man in the Middle attack, side channel attacks, Denial of Service attack. The developed algorithm is tested in real time and prevents malicious nodes from entering the network. In addition to being immune against the high level attacks the proposed algorithm functions with low communication cost. The proposed algorithm can be used for providing security in IOT devices with limited battery life and processing power such as IOT enabled and remotely deployed Wireless Sensor Networks for forest fire detection, power plant monitoring , remote military applications and many others.

Frequency-smoothing encryption: preventing snapshot attacks on deterministically encrypted data

Statistical analysis of ciphertexts has been recently used to carry out devastating inference attacks on deterministic encryption (Naveed, Kamara, and Wright, CCS 2015), order-preserving/revealing encryption (Grubbs et al., S&P 2017), and searchable encryption (Pouliot and Wright, CCS 2016). At the heart of these inference attacks is classical frequency analysis. In this paper, we propose and evaluate another classical technique, homophonic encoding, as a means to combat these attacks. We introduce and develop the concept of frequency-smoothing encryption (FSE) which provably prevents inference attacks in the snapshot attack model, wherein the adversary obtains a static snapshot of the encrypted data, while preserving the ability to efficiently and privately make point queries. We provide provably secure constructions for FSE schemes, and we empirically assess their security for concrete parameters by evaluating them against real data. We show that frequency analysis attacks (and optimal generalisations of them for the FSE setting) no longer succeed.

Frequency-smoothing encryption: preventing snapshot attacks on deterministically encrypted data

Statistical analysis of ciphertexts has been recently used to carry out devastating inference attacks on deterministic encryption (Naveed, Kamara, and Wright, CCS 2015), order-preserving/revealing encryption (Grubbs et al., S&P 2017), and searchable encryption (Pouliot and Wright, CCS 2016). At the heart of these inference attacks is classical frequency analysis. In this paper, we propose and evaluate another classical technique, homophonic encoding, as a means to combat these attacks. We introduce and develop the concept of frequency-smoothing encryption (FSE) which provably prevents inference attacks in the snapshot attack model, wherein the adversary obtains a static snapshot of the encrypted data, while preserving the ability to efficiently and privately make point queries. We provide provably secure constructions for FSE schemes, and we empirically assess their security for concrete parameters by evaluating them against real data. We show that frequency analysis attacks (and optimal generalisations of them for the FSE setting) no longer succeed.

Securing Digital Audio using Complex Quadratic Map

In This digital era, exchanging data are common and easy to do, therefore it is vulnerable to be attacked and manipulated from unauthorized parties. One data type that is vulnerable to attack is digital audio. So, we need data securing method that is not vulnerable and fast. One of the methods that match all of those criteria is securing the data using chaos function. Chaos function that is used in this research is complex quadratic map (CQM). There are some parameter value that causing the key stream that is generated by CQM function to pass all 15 NIST test, this means that the key stream that is generated using this CQM is proven to be random. In addition, samples of encrypted digital sound when tested using goodness of fit test are proven to be uniform, so securing digital audio using this method is not vulnerable to frequency analysis attack. The key space is very huge about 8.1×l031 possible keys and the key sensitivity is very small about 10-10, therefore this method is also not vulnerable against brute-force attack. And finally, the processing speed for both encryption and decryption process on average about 450 times faster that its digital audio duration.

Secure Outsourced Association Rule Mining using Homomorphic Encryption

Several techniques are used in data analysis, where frequent itemset mining and association rule mining are very popular among them. The motivation for 'Data Mining as a Service' (DMaaS) paradigm is that when the data owners are not capable of doing mining tasks internally they have to outsource the mining work to a trusted third party. Multiple data owners can also collaboratively mine by combining their databases. In such cases the privacy of outsourced data is a major issue. Here the context includes necessity of 'corporate privacy' which means other than the data, the result of mining should also preserve privacy requirements. The system proposed uses Advanced Encryption Standard (AES) to encrypt the data items before outsourcing in order to prevent the vulnerability of 'Known Plaintext' attack in the existing system. Fictitious transactions are inserted to the databases using k-anonymity method to counter the frequency analysis attack. A symmetric homomorphic encryption scheme is applied in the databases for performing the mining securely. Based on the experiments and findings, though the running time of proposed solution is slightly greater than the existing system, it provides better security to the data items. Since the computations tasks are performed by the third party server, consumption of resources at the data owners' side is very less.

Survey and analysis of security issues on RSA algorithm for digital video data

RSA is an encryption algorithm which is used for remote login session, credit card payment systems, transport layer security, secure socket layer, pretty good privacy and email security. Optimal asymmetric encryption padding is used in RSA to avoid chosen-ciphertext attack, coppersmith attack and chosen-plaintext attack. However, encryption in context of high definition video files shows repetitions of parts of ciphertext which can lead to frequency analysis attack. Key length and padding which are used in attack are according to National Institute of Standards and Technology and public key cryptography standards respectively. Security issues in RSA padding are analysed and experimental results are used in removal of defects. New architecture also prevents adaptive-chosen ciphertext attack. Integers which are generated by protocol in PKCS padding are used in new design, thus it does not create overhead in performance and challenges in handling and distribution of integers. Two XOR operations are replaced by one for more speed. RSA combined with new design of padding gets security advantages of both block and stream ciphers.

Grammar-based transformations: attack and defence

Purpose – This research aims to propose an attack that de-obfuscates codes by exploiting the properties of context-free grammars since it is important to understand the strength of obfuscation provided by context-free grammar-based obfuscators. In addition, the possibility of automatically generated transformations is explored. Design/methodology/approach – As part of our empirical investigation, a development environment for obfuscating transformations is built. The tool is used to simulate a context-free obfuscator and to devise ways of reversing such transformations. Furthermore, a theoretical investigation of subset grammars and subset languages is carried out. Findings – It is concluded that context-free grammar-based obfuscators provide limited levels of protection. Nevertheless, their application is appropriate when combined with other obfuscating techniques. Research limitations/implications – The algorithms behave as expected on a limited number of test samples. Further work is required to increase their practicality and to establish their average reliability. Originality/value – This research shows how a frequency analysis attack can threaten the security of code scrambled by context-free grammar-based obfuscators.