Format-preserving Encryption Research Articles

CAPP: Context‐Aware Privacy‐Preserving Continuous Authentication in Smartphones

ABSTRACTThe pervasive use of smart devices in everyday life has increased the risk of theft and cyberattacks, necessitating robust and continuous behavioral authentication techniques during every interactive session. Traditional authentication methods are insufficient to address these evolving threats, as they often fail to maintain security throughout the session. To address this challenge, we propose a continuous authentication scheme that leverages touch and motion sensors, incorporating user context to enhance security. However, building an effective authentication model requires creating a behavioral template by aggregating data from multiple users. Moreover, outsourcing sensitive sensor data to third‐party servers for processing introduces the risk of privacy breaches, such as behavioral profiling or the theft of personal information. To mitigate these risks, we designed CAPP—a context‐aware privacy‐preserving data outsourcing scheme for continuous authentication during live sessions. CAPP employs Format Preserving Encryption (FPE) to encrypt the sensor data, ensuring that the data's format remains intact, which allows the machine learning model to function with minimal loss of accuracy. Our solution enables secure and efficient authentication by creating a model on the server side using data from motion sensors like the accelerometer, gyroscope, and magnetometer. Our experimental results demonstrate the effectiveness of the proposed scheme, achieving an accuracy of 87.57% and an Equal Error Rate (EER) of 12.15%, while requiring only 1.69 ms to authenticate a user. These results show that CAPP preserves user privacy and provides a rapid and reliable authentication process, outperforming existing state‐of‐the‐art methods.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

DynAuthRoute: Dynamic Security for Wireless Sensor Networks

Objectives: The research aims to design an architecture for secure transmission of data in wireless sensor networks. Methods: The method involves three main pillars: authentication, data encryption, and dynamic routing. Extensive simulations have been conducted to evaluate the suggested method in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time. Findings: For authentication, a dynamic key is used to power an improved salt password hashing method. Data encryption is performed using format-preserving encryption (FPE) with the appended salt key. Dynamic routing is implemented using a cluster-based routing technique to enhance network efficiency in terms of power consumption and security. The execution time for MD5 ranges from 15 to 22 milliseconds, while for SHA-1 it ranges from 16 to 23 milliseconds and for the proposed salt key generation it is 1 to 5 milliseconds. Similarly, in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time the proposed method shows promising results in ensuring the integrity and security of transmitted encrypted data. Novelty: The presents a novel architecture with enhanced cluster head-based selection algorithm that combines dynamic key-based authentication and secure data routing to establish a safe environment for data transmission in wireless sensor networks. This research works offers a method for encrypting text with a dynamic salt key that is safe, energy-efficient, and lightweight. Keywords: Wireless Sensor Network, Dynamic Key, Authentication, Hash function, Salt algorithm, Dynamic routing, Node clustering, Format-preserving encryption

Deep-Learning-Based Neural Distinguisher for Format-Preserving Encryption Schemes FF1 and FF3

Distinguishing data that satisfy the differential characteristic from random data is called a distinguisher attack. At CRYPTO’19, Gohr presented the first deep-learning-based distinguisher for round-reduced SPECK. Building upon Gohr’s work, various works have been conducted. Among many other works, we propose the first neural distinguisher using single and multiple differences for format-preserving encryption (FPE) schemes FF1 and FF3. We harnessed the differential characteristics used in FF1 and FF3 classical distinguishers. They used SKINNY as the inner encryption algorithm for FF3. On the other hand, we employ the standard FF1 and FF3 implementations with AES encryption (which may be more robust). This work utilizes the differentials employed in FF1 and FF3 classical distinguishers. In short, when using a single 0x0F (resp. 0x08) differential, we achieve the highest accuracy of 0.85 (resp. 0.98) for FF1 (resp. FF3) in the 10-round (resp. 8-round) number domain. In the lowercase domain, due to an increased number of plaintext and ciphertext combinations, we can distinguish with the highest accuracy of 0.52 (resp. 0.55) for FF1 (resp. FF3) in a maximum of 2 rounds. Furthermore, we present an advanced neural distinguisher designed with multiple differentials for FF1 and FF3. With this sophisticated model, we still demonstrate valid accuracy in guessing the input difference used for encryption.

New Text Steganography Technique Based on Part-of-Speech Tagging and Format-Preserving Encryption

New Text Steganography Technique Based on Part-of-Speech Tagging and Format-Preserving Encryption

Network Layer Privacy Protection Using Format-Preserving Encryption

Format-Preserving Encryption (FPE) algorithms are symmetric cryptographic algorithms that encrypt an arbitrary-length plaintext into a ciphertext of the same size. Standardisation bodies recognised the first FPE algorithms (FEA-1, FEA-2, FF1 and FF3-1) in the last decade, and they have not been used for network layer privacy protection so far. However, their ability to encrypt arbitrary-length plaintext makes them suitable for encrypting selected packet header fields and replacing their original value with ciphertext of the same size without storing excessive information on the network element. If the encrypted fields carry personally identifiable information, it is possible to protect the privacy of the endpoints in the communication. This paper presents our research on using FPE for network layer privacy protection and describes LISPP, a lightweight, stateless network layer privacy protection system. The system was developed for programmable smart network interface cards (NIC) and thoroughly tested in a real network environment. We have created several implementations ranging from pure P4 to a mix of P4 and C implementations, exploring their performance and the suitability of target-independent P4 language for such processor-intensive applications. Finally, LISPP achieved line rate TCP throughput, up to 4.5 million packets per second, with the penalty of only 30 to 60 microseconds of additional one-way delay, proving that it is adequate for use in production networks. The most efficient implementation was with the FF3-1 algorithm developed in C and carefully adapted to the specific hardware configuration of the NIC.

Neutralization Method of Ransomware Detection Technology Using Format Preserving Encryption.

Ransomware is one type of malware that involves restricting access to files by encrypting files stored on the victim's system and demanding money in return for file recovery. Although various ransomware detection technologies have been introduced, existing ransomware detection technologies have certain limitations and problems that affect their detection ability. Therefore, there is a need for new detection technologies that can overcome the problems of existing detection methods and minimize the damage from ransomware. A technology that can be used to detect files infected by ransomware and by measuring the entropy of files has been proposed. However, from an attacker's point of view, neutralization technology can bypass detection through neutralization using entropy. A representative neutralization method is one that involves decreasing the entropy of encrypted files by using an encoding technology such as base64. This technology also makes it possible to detect files that are infected by ransomware by measuring entropy after decoding the encoded files, which, in turn, means the failure of the ransomware detection-neutralization technology. Therefore, this paper derives three requirements for a more sophisticated ransomware detection-neutralization method from the perspective of an attacker for it to have novelty. These requirements are (1) it must not be decoded; (2) it must support encryption using secret information; and (3) the entropy of the generated ciphertext must be similar to that of plaintext. The proposed neutralization method satisfies these requirements, supports encryption without decoding, and applies format-preserving encryption that can adjust the input and output lengths. To overcome the limitations of neutralization technology using the encoding algorithm, we utilized format-preserving encryption, which could allow the attacker to manipulate the entropy of the ciphertext as desired by changing the expression range of numbers and controlling the input and output lengths in a very free manner. To apply format-preserving encryption, Byte Split, BinaryToASCII, and Radix Conversion methods were evaluated, and an optimal neutralization method was derived based on the experimental results of these three methods. As a result of the comparative analysis of the neutralization performance with existing studies, when the entropy threshold value was 0.5 in the Radix Conversion method, which was the optimal neutralization method derived from the proposed study, the neutralization accuracy was improved by 96% based on the PPTX file format. The results of this study provide clues for future studies to derive a plan to counter the technology that can neutralize ransomware detection technology.

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

Biometric based authentication systems are being prominently used everywhere. The biometric data, popularly known as a biometric template, is generally stored on the database server in its unprotected form. Unlike passwords, once compromised, biometric data can never be recovered. An ideal biometric system should provide accessibility, acceptability, availability, high security, and high biometric performance to the user. Current deployments generally relax in one or more requirements, resulting in lingering concerns about the privacy and security of individuals’ biometric data. Our proposed work introduces a cancelable biometric template protection scheme based on the format-preserving encryption and Bloom filters. The format-preserving encryption encrypts the biometric template, which then maps to the Bloom filter based template that represents the cancelable template. The use of format-preserving encryption along with Bloom filters helps to achieve the security of the input biometric template and identification with good recognition performance. We achieve 0.2% FRR at 0.01% FAR for IITD-CASIA virtual dataset in the uni-biometric scenario. A comparison with the existing schemes shows that our proposed scheme exhibits high recognition performance for both uni-biometric and multi-biometric datasets, while simultaneously, the security of the overall system is preserved.

New Text Steganography Technique based on Multilayer Encoding with Format-Preserving Encryption and Huffman Coding

Steganography is the process of hiding secret data inside other media or cover media. Balancing the requirements for capacity, security, and imperceptibility is the main challenge for any successful steganography system. In text steganography, the data hiding capacity is limited because of the lack of redundant data compared to other digital media, such as images, video, or audio. Other challenges in text steganography are imperceptibility and security. Poor imperceptibility results from the structure of the text file, which is more visually apparent in terms of syntax and grammar than in other media. Low level of security results from the sequential selection of positions for embedding secret data due to insufficient redundant data in a text file. Therefore, an attacker or a third party would notice slight changes in the text file. This paper proposes a new text steganography method that combines cryptography and compression techniques to deal with these issues. This technique is used to conceal secret data to achieve high data hiding capacity in the cover text while maintaining security and imperceptibility. Multilayer encoding and Format-Preserving Encryption (FPE) with Huffman Coding, are applied to secret data before embedding. Invisible Unicode characters are employed to embed secret data into English text files to generate stego files. Results show that the proposed method satisfies capacity and imperceptibility in the cover file by comparing it with previously developed methods.

Masked Implementation of Format Preserving Encryption on Low-End AVR Microcontrollers and High-End ARM Processors

Format-Preserving Encryption (FPE) for Internet of Things (IoT) enables the data encryption while preserving the format and length of original data. With these advantages, FPE can be utilized in many IoT applications. However, FPE requires complicated computations and these are high overheads on IoT embedded devices. In this paper, we proposed an efficient implementation of Format-preserving Encryption Algorithm (FEA), which is the Korean standard of FPE, and the first-order masked implementation of FEA on both low-end (i.e., AVR microcontroller) and high-end (i.e., ARM processor) IoT devices. Firstly, we show the vulnerability of FEA when it comes to the Correlation Power Analysis (CPA) approach. Afterward, we propose an efficient implementation method and the masking technique for both low-end IoT device and high-end IoT device. The proposed method is secure against power analysis attacks but the performance degradation of masked measure is only 2.53∼3.77% than the naïve FEA implementation.

Comment on ‘Targeted Ciphers for Format‐Preserving Encryption’ from Selected Areas in Cryptography 2018

Comment on ‘Targeted Ciphers for Format‐Preserving Encryption’ from Selected Areas in Cryptography 2018

SmartCAMPP - Smartphone-based continuous authentication leveraging motion sensors with privacy preservation

Abstract Continuous Authentication (CA) approaches are attracting attention due to the explosion of available sensors from IoT devices such as smartphones. However, a critical privacy concern arises when CA data is outsourced. Data from motion sensors may reveal users’ private issues. Despite the need for CA in smartphones, no previous work has explored how to tackle this matter leveraging motion sensors in a privacy-preserving way. In this work, a mechanism dubbed SmartCAMPP is proposed to achieve CA based on gyroscope and accelerometer data. Format-preserving encryption techniques are applied to privately outsource them. Our results show the suitability of the proposed scheme, featuring 76.85 % of accuracy while taking 5.12 ms. of computation for authenticating each user. Interestingly, the use of cryptography does not lead to a significant impact as compared to a non-privacy-preserving mechanism.

형태보존암호 FEA 알고리즘 구현 및 성능 평가

블록 암호는 암호화를 할 때 길이가 증가하여 블록 크기의 배수가 된다. 따라서 블록 암호는 IoT나 커넥티드카 등 고정된 길이와 구조를 가진 데이터를 암호화하기 어려운 문제가 있다. 형태보존암호는 이와 같은 블록 암호의 문제점을 해결하여 효율적으로 암호화 할 수 있다. 형태보존암호 FEA는 국내에서 개발된 형태보존암호이다. FEA는 랭킹 함수, TBC 암호화, 역 랭킹 함수 단계로 암 · 복호화되지만 논문과 표준에서는 랭킹 함수의 정의가 되어있지 않고 사용자가 직접 랭킹 함수를 구현하여 사용하도록 설계되었다. 본 논문은 랭킹 함수를 직접 구현하여 FEA의 전 과정을 구현하였다. 구현한 FEA가 정상적으로 암 · 복호화되는지 확인하였고, 미국 NIST 표준 형태보존 암호와 비교하여 암 · 복호화 속도가 더 빠른 것을 확인하였다.

Privacy Protection in Enterprise Social Networks Using a Hybrid De-Identification System

Enterprise social networks (ESN) have been widely used within organizations as a communication infrastructure that allows employees to collaborate with each other and share files and documents. The shared documents may contain a large amount of sensitive information that affect the privacy of persons such as phone numbers, which must be protected against any kind of disclosure or unauthorized access. In this study, authors propose a hybrid de-identification system that extract sensitive information from textual documents shared in ESNs. The system is based on both machine learning and rule-based classifiers. Gradient boosted trees (GBTs) algorithm is used as machine learning classifier. Experiments ran on a modified CoNLL 2003 dataset show that GBTs algorithm achieve a very high F1-score (95%). Additionally, the rule-based classifier is consisted of regular expression and gazetteers in order to complement the machine learning classifier. Thereafter, the sensitive information extracted by the two classifiers are merged and encrypted using Format Preserving Encryption method.

BIOFUSE: A framework for multi-biometric fusion on biocryptosystem level

Biometric cryptosystems or biocryptoystems are gaining prominence for cryptographic key generation, encryption and biometric template protection. However, the most popular state-of-the-art biocryptosystems- fuzzy commitment and fuzzy vault are prone to multiple security attacks. Recently proposed multi-biometric cryptosystems improve security and enhance recognition performance. They perform the fusion of multi-biometric characteristics with either a single biocryptosystem or independently accessed, multiple biocryptosystems. An attack on any of the involved biocryptosystems can weaken the security of the whole system. In our paper, we propose a multi-biometric fusion framework- BIOFUSE, that combines fuzzy commitment and fuzzy vault using the format-preserving encryption scheme. BIOFUSE makes it improbable for an attacker to get unauthorized access to the system without impersonation of all the biometric inputs of the genuine user at the same instant. We present 4 most basic ways of constructing BIOFUSE and found only 1 named S-BIOFUSE (S3) as a secure design. We compare the recognition performance of the proposed scheme with existing multi-biometric cryptosystems on various databases. The results show 0.98 true match rate at 0.01 false match rate on a virtual IITD-DB1 database that indicates that our proposed work achieves significantly good recognition performance while providing high security.

Privacy Preserving for Sensitive Data using Data Masking Technique

In recent times, most the people are using internet where they are going to share sensitive information with other individual or with an organization like hospital, banking sector or business companies. So such huge amount of information will be stored on cloud. The attackers may try to hack the sensitive data and will try to misuse that data. So here the security for data comes first. There are numerous methods available to provide security for the data that is being shared among individuals or organizations. Most of the organizations take enough precautions to secure data that is shared with third party organizations. In recent times providing privacy for the sensitive data is high priority. The objective of this research is to discover the various data masking solutions for different applications for providing security to the data. Established data privacy method like AES or DES encryption technique proves to be proficient but time consuming. In order to avoid time consumption and to provide privacy for the data being shared, this paper proposes a information hiding method based on format-preserving encryption for sensitive data. This method will masquerade only sensitive data and make sure the encrypted data is still in the original format where it doesn’t consume much memory space. Organization like hospitals or banking sector or any business companies can use this format-preserving method to enhance the security of the data being shared. Tested the information on Spark illustrate that information hiding method based on format-preserving encryption can provide data privacy for sensitive data and preserve data format.

Partial image encryption using format-preserving encryption in image processing systems for Internet of things environment

Concomitant with advances in technology, the number of systems and devices that utilize image data has increased. Nowadays, image processing devices incorporated into systems, such as the Internet of things, drones, and closed-circuit television, can collect images of people and automatically share them with networks. Consequently, the threat of invasion of privacy by image leakage has increased exponentially. However, traditional image-security methods, such as privacy masking and image encryption, have several disadvantages, including storage space wastage associated with data padding, inability to decode, inability to recognize images without decoding, and exposure of private information after decryption. This article proposes a method for partially encrypting private information in images using FF1 and FF3-1. The proposed method encrypts private information without increasing the data size, solving the problem of wasted storage space. Furthermore, using the proposed method, specific sections of encrypted images can be decrypted and recognized before decryption of the entire information, which addresses the problems besetting traditional privacy masking and image encryption methods. The results of histogram analysis, correlation analysis, number of pixels change rate, unified average change intensity, information entropy analysis, and NIST SP 800-22 verify the security and overall efficacy of the proposed method.

ShadowFPE: New Encrypted Web Application Solution Based on Shadow DOM

Most of users hesitate to use third-party web applications because of security and privacy concerns. An ideal solution would be to allow apps to work with encrypted data, so that users might be more willing to provide just the encrypted version of their sensitive data. ShadowCrypt, proposed in CCS 2014, is the first and so far only solution that can achieve this by leveraging the encapsulation provided by Shadow DOM V0, without the need for the users to trust neither server nor client codes of web applications. Unfortunately, researchers have shown that ShadowCrypt is vulnerable to several attacks. Note that ShadowCrypt is no longer compliant to the updated W3C standard since 2015. Furthermore, some attacks on ShadowCrypt have been proposed. Hence, currently there is no effective and secure solution to guarantee the privacy of users. In this paper, we present ShadowFPE, a novel format-preserving encryption that makes use of a robust property in Shadow DOM to obtain a feasible solution. Compared with ShadowCrypt, ShadowFPE does not destroy the data format and makes the data usable in most of cloud web applications. We confirmed the effectiveness and security of ShadowFPE through case studies on web applications. Our results show that ShadowFPE is practical since it has low computational overhead and requires minimal modification in existing applications.

A New Method for Format Preserving Encryption in High-Data Rate Communications

In some encryption systems it is necessary to preserve the format and length of the encrypted data. This kind of encryption is called FPE (Format Preserving Encryption). Currently, only two AES (Advanced Encryption Standard) modes of operation recommended by the NIST (National Institute of Standards and Technology) are able to implement FPE algorithms, FF1 and FF3. These modes work in an electronic codebook fashion and can be configured to encrypt databases with an arbitrary format and length. However, there are no stream cipher proposals able to implement FPE encryption for high data rate information flows. The main novelty of this work is a new block cipher operation mode proposal to implement an FPE algorithm in a stream cipher fashion. It has been called CTR-MOD and it is based on a standard block cipher working in CTR (Counter) mode and a modulo operation. The confidentiality of this mode is analyzed in terms of its IND- CPA (Indistinguishability under Chosen Plaintext Attack) advantage of any adversary attacking it. Moreover, the encryption scheme has been implemented on an FPGA (Field Programmable Gate Array) and has been integrated in a Gigabit Ethernet interface to test an encrypted optical link with a real high data rate traffic flow.

A Novel Message-Preserving Scheme with Format-Preserving Encryption for Connected Cars in Multi-Access Edge Computing.

In connected cars with various electronic control unit (ECU) modules, Ethernet is used to communicate data received by the sensor in real time, but it is partially used alongside a controller area network (CAN) due to the cost. There are security threats in the CAN, such as replay attacks and denial-of-service attacks, which can disrupt the driver or cause serious damage, such as a car accident through malicious manipulation. Although several secure protocols for protecting CAN messages have been proposed, they carry limitations, such as combining additional elements for security or modifying CAN messages with a limited length. Therefore, in this paper, we propose a method for encrypting the data frame, including real data in the CAN message structure, using format-preserving encryption (FPE), which ensures that the plaintext and ciphertext have the same format and length. In this way, block ciphers such as AES-128 must be divided into two or three blocks, but FPE can be processed simultaneously by encrypting them according to the CAN message format, thus providing better security against denial-of-service attacks. Based on the 150 ms CAN message, a normal message was received from a malicious message injection of 180 ms or more for AES-128 and a malicious message injection of 100 ms or more for FPE. Finally, based on the proposed scheme, a CAN transmission environment is constructed for analyzing the encryption/decryption rate and the process of transmitting and processing the encrypted message for connected cars in multi-access edge computing (MEC). This scheme is compared with other algorithms to verify that it can be used in a real environment.