Ciphertext Data Research Articles

Data Secure De-Duplication in Cloud Environment

In the current area of information explosion, users’ demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Cloud storage facilitates users to backup and share data, effectively reducing users’ storage expenses. As the duplicate data of different users are stored multiple times, leading to a sudden decrease in storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to store data after encryption to protect user privacy. In this paper, we focus on how to achieve secure re-duplication and recover data in ciphertext for different users, and determine whether the indexes of public key searchable encryption and the matching relationship of trapdoor are equal in cipher text to achieve secure de-duplication. For the duplicate file, the data user’s re-encryption key about the file is appended to the ciphertext chain table of the stored copy. The cloud server uses the re-encryption key to generate the specified transformed ciphertext, and the data user decrypts the transformed ciphertext by its private key to recover the file. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis.

Ensuring Privacy and Recovery: Public Key Encryption for Secure Data De-Duplication with Keyword Search

In the current era of information explosion, user’s demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Clous storage facilitates the backup and sharing of data, efficiently reducing user’s storage expenses. The duplicate data of different users is stored multiple times, leading to a sudden decrease in the storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to achieve secure de-duplication and recover data in cipher text for different users, and relationship of trapdoor are equal in cipher text to achieve secure de-duplication. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis. Keywords: Data De-duplication, Mobile Edge Computing, Authentication, Security, Cryptographic Techniques, Data Integrity.

Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption

ABSTRACT Smart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

Delegated quantum neural networks for encrypted data

Quantum machine learning is expected to utilize the potential advantages of quantum computing to advance the efficiency of machine learning. However, with the help of quantum cloud servers, ordinary users may confront the threat of privacy leakage of input data and models when performing the training or inference of quantum neural networks (QNNs). To address this problem, we present a new framework that allows the training and inference of delegated QNNs to be performed on encrypted data to protect the privacy of users’ data and models. This framework contains two models that are alternately trained: an encryptor and a predictor. The classical client first trains the encryptor defined by a classical neural network to map plaintext input data to vastly different ciphertext data. The ciphertext data is sent to the quantum cloud server to train the predictor defined by a QNN, which can indirectly predict the labels of plaintext data. With the trained encryptor and predictor, the client can send the encrypted data to the server for prediction and obtain almost equivalent prediction results. The proposed framework is applied to three types of QNN models, each dealing with low-dimensional tabular data, image data, and one-dimensional time series data, respectively. Experimental results show that the privacy protection method based on our framework can protect data and model privacy without degrading the performance of QNNs. The framework does not require users to have quantum capabilities and is suitable for protecting data and model privacy for various QNN models.

Advanced Authentication and Energy-Efficient Routing Protocol for Wireless Body Area Networks

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients’ data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient’s private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models.

The Utilization of Homomorphic Encryption Technology Grounded on Artificial Intelligence for Privacy Preservation

The advent of Artificial Intelligence (AI) and Machine Learning (ML), particularly deep learning, has escalated the demand for computing resources. However, the high hardware requirements pose challenges for companies, compelling them to outsource ML tasks to the cloud. Nevertheless, concerns about cloud trustworthiness limit such applications. Encrypting data before uploading it to the cloud is a straightforward solution to ensure data security. However, traditional encryption schemes render ciphertext data unable to participate in operations within the ciphertext domain, posing challenges for data analysis. This paper delves into the pivotal role of homomorphic encryption in addressing the critical issue of privacy protection in machine learning.

Chebyshev polynomial approximation in CNN for zero-knowledge encrypted data analysis

Integrating Deep Learning (DL) techniques in Convolutional Neural Networks (CNNs) with encrypted data analysis is an emerging field for enhancing data privacy and security. A significant challenge in this domain is the incompatibility of standard non-linear Activation Functions (AF) like Rectified Linear Unit (ReLU) and Hyperbolic Tangent (tanh) with Zero-Knowledge (ZK) encrypted data, which impacts computational efficiency and data privacy. Addressing this, our paper introduces the novel application of Chebyshev Polynomial Approximation (CPA) to adapt these AF to process encrypted data effectively. Utilizing the MNIST dataset, this paper conducted experiments with LeNet and various configurations of AlexNet, extending the range of the ReLU and tanH functions to optimize CPA. Our results reveal an optimal polynomial degree (α), with α = 10 for ReLU and between α = 10 and α = 15 for tanH, beyond which the benefits in accuracy plateau. This finding is crucial for ensuring the accuracy and efficiency of CNNs in processing encrypted data. This recommended study demonstrates that while the accuracy slightly decreases for plaintext data and more significantly for ciphertext data, the overall effectiveness of CPA in CNNs is maintained. This advancement enables CNNs to process encrypted data while preserving privacy and marks a significant step in developing privacy-preserving Machine Learning (ML) and encrypted data analysis.

Improved secure PCA and LDA algorithms for intelligent computing in IoT‐to‐cloud setting

AbstractThe rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments.

Multi-Keyword Searchable Identity-Based Proxy Re-Encryption from Lattices

To protect the privacy of cloud data, encryption before uploading provides a solution. However, searching for target data in ciphertext takes effort. Therefore, searchable encryption has become an important research topic. On the other hand, since the advancement of quantum computers will lead to the crisis of cracking traditional encryption algorithms, it is necessary to design encryption schemes that can resist quantum attacks. Therefore, we propose a multi-keyword searchable identity-based proxy re-encryption scheme from lattices. In addition to resisting quantum attacks, the proposed scheme uses several cryptographic techniques to improve encryption efficiency. First, identity-based encryption is used to reduce the computation and transmission costs caused by certificates. Second, the proposed scheme uses proxy re-encryption to achieve the purpose of outsourced computing, allowing the proxy server to reduce the computation and transmission costs of the users. Third, the proposed multi-keyword searchable encryption can provide AND and OR operators to increase the flexibility of searchability. Moreover, the access structure of the proposed scheme is not based on a linear secret sharing scheme (LSSS), avoiding the errors caused by an LSSS-based structure in decryption or search results. Finally, we also give formal security proof of the proposed scheme under the decisional learning with errors assumption.

Certificateless Searchable Encryption With Trapdoor Unlinkability for Industrial Internet of Things

Industrial Internet of Things (IIoT) is a promising application of Internet of Things (IoT) in the industrial sector, which greatly improves the manufacturing efficiency and enables the intelligent production. Nowadays, cloud computing has been introduced to handle the data management problem in IIoT. Nevertheless, how to retrieve ciphertext data remains a challenging problem. Recently, certificateless searchable encryption (CLSE) was proposed to tackle with this problem. CLSE allows the receiver to search the ciphertext data directly, meanwhile it protects the data privacy. However, existing CLSE schemes do not achieve the trapdoor unlinkability since deterministic algorithms are used to generate the trapdoors. Additionally, they cannot support multiple keywords search in one-time test operation. To address these issues, we propose a certificateless searchable encryption scheme with trapdoor unlinkability (CLSETU) for IIoT. We then demonstrate the semantic security of our scheme against insider keyword guessing attacks (KGA). Finally, the performance evaluation shows its feasibility in the deployment of IIoT.

A computer-aided feature-based encryption model with concealed access structure for medical Internet of Things

One of the Internet of Things (IoT) security issues is the secure sharing and granular management of data access. This study recommends a feature-based encryption scheme with a hidden access structure for medical IoT data security. While establishing fine-grained access control of ciphertext data, the system can guarantee clinical client data privacy. First, it is recommended to convert identity-based encryption (IBE) into a feature-based encryption model (FBEM) using a universal conversion technique that supports multi-valued attributes and gates. IBE characteristics could be inherited by the converted FBEM. The conversion method is then used to change the receiver anonymous IBE scheme into the FBEM scheme with concealed access structure. The FBEM model is then used to construct the IoT scenario for the smart medical application. Theoretical analysis and experimental findings reveal that the suggested system provides advantages over prominent systems regarding computing efficiency, storage load, and security when the access structure is disguised.

A lightweight encrypted deduplication scheme supporting backup

Data deduplication technology is frequently used by many cloud service providers to handle the exponential growth in data. To protect their data privacy, users frequently encrypt their data before uploading it to a cloud storage provider. Therefore, efficient encrypted deduplication technology has been widely studied. Although cloud storage provides convenience for users, it also has the problem of high data concentration. Once an accident occurs in the data center, the impact is enormous. Therefore, it is necessary to back up the ciphertext data after deduplication. In this paper, we propose an efficient encrypted data deduplication technology that supports backup. Our scheme uses a one-way hash chain to verify the legitimacy of a user’s identity. It updates the user’s ownership of the data using the user’s index table. In this paper, we use the XOR operation to re-encrypt data to obtain backup ciphertext, reducing computational complexity. In LEDB, the user can extract the key from the data label. Therefore, the user does not need to manage the key separately. In addition, LEDB supports a two-level deduplication mechanism, which can not only ensure transmission security but also reduce network transmission bandwidth. Finally, LEDB allows the user to determine the integrity of the data before decrypting the ciphertext. Security analysis and experimental results show that our scheme is secure and efficient for data encryption and decryption.

Secure-by-design serverless workflows on the Edge–Cloud Continuum through the Osmotic Computing paradigm

Nowadays, many Cloud companies adopt serverless computation based on the Function as a Service (FaaS) paradigm. Such an approach, built on the dynamic provisioning of serverless functions, has significantly altered the design of Cloud applications. Indeed, complex systems may now be viewed as a graph of serverless functions (i.e., workflow) distributed over the Cloud and Edge layers. However, building applications in such a complicated context and introducing security by design is still a non-trivial problem. Meanwhile, Osmotic Computing has advanced as a novel computational paradigm in recent years. This may serve as a suitable foundation for strengthening security in serverless applications throughout the Cloud–Edge Continuum. In this work, we rely on Osmotic Computing principles for modifying the architecture of OpenWolf, a novel serverless engine, and improving the execution time of ciphertext data by about 65%.

A flexible and visually meaningful multi-image compression, encryption and hiding scheme based on 2D compressive sensing

Background: Encrypting plain images into noise-like cipher images is a common method in image encryption. However, when noise-like images appear in public networks, they are more likely to attract attention and suffer more cryptanalysis. To solve this problem, researchers propose the concept of visually meaningful image encryption scheme, which encrypts a plain image into a visually meaningful cipher image. Objective: In order to realize the visual security of cipher image and increase information capacity, this paper proposes a flexible visually secure multi-image compression, encryption and hiding scheme based on two-dimensional compressive sensing (2DCS), which can flexibly complete the compression and encryption of multiple plain images without increasing the amount of ciphertext data. Methods: The scheme is divided into encryption process and embedding process. In the encryption process, the plain image is randomly scrambled and non-linear gray value transformed to obtain a pre-encrypted integer matrix, then 2DCS is used to compress the pre-encrypted integer matrix to get the secret image. Repeat this process for multiple plain images to obtain multiple secret images. In the embedding process, integer wavelet transform and bit-plane decomposition are used to embed multiple secret images into the quantized coefficient matrix of host image to get the modified coefficient matrix, and then the inverse integer wavelet transform is used to transform the modified coefficient matrix into spatial space to get the visually meaningful cipher image. Result: The simulation experiment verifies the feasibility and effectiveness of the visually meaningful multi-image encryption scheme, and users can choose to improve the system's encryption capacity or cipher image's visual security according to their own needs.

Edge-aided searchable data sharing scheme for IoV in the 5G environment

The development of 5G technology provides huge potential for the Internet of Vehicles (IoV). In the IoV scenario, the secure exchange of traffic warning data is necessary to guarantee the safety of pedestrians, vehicles, and public transport facilities. However, there remain a series of challenging issues to achieve data secure sharing while ensuring efficiency. Current solutions such as the data outsourcing storage of traditional centralized cloud servers may cause information leakage due to their incomplete credibility. To address these problems mentioned above, this paper proposes an edge-aided searchable data sharing scheme based on blockchain for IoV in a 5G environment. First, the proposed scheme stores the search index of the keyword and the hash value of the data ciphertext on the blockchain to ensure its integrity and authenticity, and the smart contract automatically verifies the search trapdoor. And then, an edge caching mechanism is designed to quickly feedback on users’ retrieval needs based on a cloud-edge collaboration model. In addition, attribute-based searchable encryption is used to ensure that only the users who meet the access attributes and policy can decrypt the encrypted traffic warning data, which can achieve fine-grained access control and keyword search simultaneously. Finally, security analysis and performance evaluation demonstrate that our scheme is secure and more efficient compared with other schemes.

Data Secure De-Duplication and Recovery Based on Public Key Encryption With Keyword Search

In the current era of information explosion, users’ demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Cloud storage facilitates users to backup and share data, effectively reducing users’ storage expenses. As the duplicate data of different users are stored multiple times, leading to a sudden decrease in storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to store data after encryption to protect user privacy. In this paper, we focus on how to achieve secure de-duplication and recover data in ciphertext for different users, and determine whether the indexes of public key searchable encryption and the matching relationship of trapdoor are equal in ciphertext to achieve secure de-duplication. For the duplicate file, the data user’s re-encryption key about the file is appended to the ciphertext chain table of the stored copy. The cloud server uses the re-encryption key to generate the specified transformed ciphertext, and the data user decrypts the transformed ciphertext by its private key to recover the file. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis.

Block Cipher Identification Scheme Based on Hamming Weight Distribution

In order to solve the problem of worse recognition performance under the multi-classification scenarios of existing cryptosystem identification, this paper proposes a block cryptosystem recognition scheme based on Hamming weight distribution. A feature extraction method based on Hamming weight distribution is designed to extract the cipher text features, and the cryptosystem is further described. XGBoost-RFE feature selection algorithm is proposed to filter invalid features. In order to better adapt to the characteristics of complex ciphertext data and difficult fitting in multi classification scenarios, XGB-LGBM ensemble learning model with multi-layer fusion structure is designed to further improve the accuracy and generalization of recognition. The experiment carried out mixed identification of 10 common block cipher algorithm, and the overall recognition accuracy reached 89.65%.

Ciphertext Database Audit Technology Under Searchable Encryption Algorithm and Blockchain Technology

The study aims to solve the problems in auditing ciphertext data, improve audit efficiency, and increase the security of audit data in the audit server. First, the existing encryption algorithms are analyzed. Second, the searchable encryption algorithm is proposed to audit the ciphertext data, and an audit server scheme is made based on blockchain technology (BT). Finally, the two schemes are compared with the traditional audit technology. The results show that the server's inspection efficiency of the searchable encryption algorithm is higher.

Chaos-Based Lightweight Cryptographic Algorithm Design and FPGA Implementation.

With the massive application of IoT and sensor technologies, the study of lightweight ciphers has become an important research topic. In this paper, an effective lightweight LZUC (lightweight Zu Chongzhi) cipher based on chaotic system is proposed to improve the traditional ZUC algorithm. In this method, a further algorithm is designed for the process of integrating chaos into the lightweighting of ZUC. For the first time, this design introduces the logistic chaotic system into both the LFSR (linear feedback shift register) and nonlinear F-function of the cryptographic algorithm. The improved LZUC algorithm not only achieves a certain effect in lightweighting, but also has good statistical properties and security of the output sequence. To verify the performance of the LZUC cipher, we performed NIST statistical tests and information entropy analysis on its output key streams and discussed the typical attacks on the algorithm's resistance to weak key analysis, guess-determination analysis, time-stored data trade-off analysis, and algebraic analysis. In addition, we completed the design of an image security system using the LZUC cipher. Histogram analysis and correlation analysis are used to analyze both plaintext and ciphertext data. At the end of the article, the plaintext and ciphertext images displayed by LCD can be further visualized to verify the encryption effectiveness of the LZUC cipher.

EEPPDA—Edge‐enabled efficient privacy‐preserving data aggregation in smart healthcare Internet of Things network

AbstractThe Internet of Things‐based smart healthcare provides numerous facilities to patients and medical professionals. Medical professionals can monitor the patient's real‐time medical data and diagnose diseases through the medical health history stored in the cloud database. Any kind of attack on the cloud database will result in misdiagnosis of the patients by medical professionals. Therefore, it becomes a primary concern to secure private data. On the other hand, the conventional data aggregation method for smart healthcare acquires immense communication and computational cost. Edge‐enabled smart healthcare can overcome these limitations. The paper proposes an edge‐enabled efficient privacy‐preserving data aggregation (EEPPDA) scheme to secure health data. In the EEPPDA scheme, captured medical data have been encrypted by the Paillier homomorphic cryptosystem. Homomorphic encryption is engaged in the assurance of secure communication. For data transmission from patients to the cloud server (CS), data aggregation is performed on the edge server (ES). Then aggregated ciphertext data are transmitted to the CS. The CS validates the data integrity and analyzes and processes the authenticated aggregated data. The authorized medical professional executes the decryption, then the aggregated ciphertext data are decrypted in plaintext. EEPPDA utilizes the batch verification process to reduce communication costs. Our proposed scheme maintains the privacy of the patient's identity and medical data, resists any internal and external attacks, and verifies the health data integrity in the CS. The proposed scheme has significantly minimized computational complexity and communication overhead concerning the existing approach through extensive simulation.