Ciphertext Data Research Articles

SECURE FILE STORAGE ON CLOUD USING HYBRID CRYPTOGRAPHY

The proposed model is liable to meet the required security needs of data centred of cloud. AES, DES and RSA are used for the encryption of file slices takes minimum time and has maximum throughput for encryption and decryption from other symmetric algorithms. The idea of splitting and merging adds on to meet the principle of data security. The hybrid approach when deployed in cloud environment makes the remote server more secure and thus, helps the cloud providers to fetch more trust of their users. For data security and privacy protection issues, the fundamental challenge of separation of sensitive data and access control is fulfilled. Cryptography technique translates original data into unreadable form. Cryptography technique is divided into symmetric key cryptography and public key cryptography. This technique uses keys for translate data into unreadable form. So only authorized person can access data from cloud server. Cipher text data is visible for all people.

A Multiuser Ciphertext Search Scheme Based on Blockchain and SGX

To ensure the security of data, more and more users encrypt data for storage, which makes the high-efficiency ciphertext search problem in the context of cloud storage a research hotspot. Existing solutions still suffer from many vexatious problems, such as the need to maintain complex index structures and the unsatisfactory application of homomorphic schemes. To solve the above problems, this paper proposes a multiuser ciphertext search scheme based on blockchain and SGX. Our scheme uses blockchain and SGX to protect keywords and data privacy and complete decryption and keywords search of ciphertext data which does not need pregenerated indexes or preselected keywords. Second, for a multiuser scenario, a smart contract is designed to verify authorization requests and manage multiple authorized users. Finally, we give security analysis, function comparison, and performance analysis to prove the security and feasibility of our scheme. Experiments show that our scheme has effectively met practical requirements.

SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things

In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge.As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant’s resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant’s computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme’s security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme’s universality.

Flexible privacy‐preserving machine learning: When searchable encryption meets homomorphic encryption

Now various privacy-preserving techniques have been combined with machine learning to ensure training data security. However, during the training, users were unable to select data and labels adaptively in the server, which made the trained models challenging in meeting user needs. Repeated model training not only wastes server resources, but also reduces query efficiency. In this paper, we combine two privacy-preserving technologies, searchable encryption and homomorphic encryption, and propose a highly flexible machine training framework. Homomorphic encryption technology is used to encrypt data, and symmetric searchable encryption technology is used to generate blind indexes and trapdoors. The server can perform ciphertext data search through blind query trapdoors and indexes, and can use the searched homomorphic ciphertext for model training. This framework significantly improves the flexibility of training and the fit of the model, and also ensures the security of dynamic data management. The server locally uses the header of the query trap and the storage address of the ciphertext model to build an automatically updatable ciphertext model table. On the basis of ensuring the effectiveness of the model, it greatly improves the efficiency of obtaining the model, effectively prevents redundant training when different users send the same request, and reasonably allocates resources in the server.

The Security of Database Network Model Based on Fractional Differential Equations

Abstract This paper discusses a class of inhomogeneous fractional differential equations with boundary conditions. At the same time, we apply it to the database network encryption model. In this paper, the fractional differential equation algorithm segments the big data in the cloud environment, and the homomorphic algorithm is used for encryption. We encrypt the algorithms and store them in the database, respectively. When the model is queried, the corresponding ciphertext data in the database can be matched according to the type of query statement. The experimental simulation realizes the information retrieval and statistics in the case of full ciphertext on the server-side.

Reversible Data Hiding in Encrypted Images Based on Adaptive Gradient Prediction

The existing reversible data hiding (RDH) technology solves the problem that the ciphertext data and carrier are easily damaged in the traditional scheme, which has become a research hotspot in information hiding. However, because of the small coverage area of the predictor and poor prediction ability of the existing reversible data hiding in encrypted images (RDH-EI) technology based on pixel predictor, the embedding performance of the algorithm is limited. Therefore, this paper proposes an adaptive gradient prediction (AGP) scheme. The AGP employs a comprehensive and efficient local complexity measurement strategy to make predictions based on pixel changes around the predicted pixel, including horizontal, vertical, and diagonal directions. The experimental results show that the AGP-RDHEI scheme has apparent advantages in embedding rate.

Secure the File Storage on Cloud Computing Using Hybrid Cryptography Algorithm

Abstract: The proposed of this paper is for the security needs of the cloud data center. Blowfish is used to convert encrypt file cuts, takes the minimum amount of time, and has the extreme throughput for encryption and decryption from other compatible algorithms. The idea of spreading and mixing meets the principle of information security. The hybrid approach makes the remote servers more secure when deployed in a cloud environment and, thus helps cloud providers gain more trust from their users. Used for data safety and privacy issues, the basic challenge of separating sensitive data and access control has been met. Cryptography techniques translate original data into an unreadable format. The encryption technique is divided into private key encryption and public-key encryption. Here we will use AES 128-algorithm, DES, RC, and LSB encryption techniques. This technique uses keys to translate data into an unreadable format. Therefore only authorized persons can access data from the cloud server. Ciphertext data is visible to all. Keywords: Cloud, AES, RSA, Blowfish algorithms, Encryption/Decryption Techniques.

Efficient Multibit Function Encryption for Data Security in Internet of Things

The development of the Internet of Things (IoT) has been facing severe security threats, and the security and fine-grained access control of data in the IoT is one of the security problems that urgently need to deal with. Attribute-based encryption (ABE) schemes over lattice can not only achieve fine-grained access control but also resist quantum attacks. However, most schemes are single-bit encryption, which is inefficient. In this study, a multibit inner product predicate encryption (PE) scheme over lattice is proposed, which effectively expands the plaintext space. The scheme can realize multibit attribute-based encryption with the hidden access structure for data security in the IoT and support And-gate operation in the access structure with multiattribute. The fine-grained access control of ciphertext data can be realized under the condition of ensuring data privacy. The security of the scheme is based on LWE problem, and it can resist quantum attacks, that is, CPA security under the standard model.

Computer Vision Operating System of Bank Economic Management Security under 5G Wireless Communication Technology

Based on 5G wireless communication technology, this paper proposes a secure online banking payment scheme using a password algorithm and identity authentication+SMS verification code. We use the lightweight block cipher algorithm to construct a numeric permutation table. Then, we use the digital permutation table to perform the permutation encryption operation to obtain the digital ciphertext data. Finally, we connect 5G radiofrequency identification, identity authentication, and other modules to the trusted computing platform. The research found that the online banking security encryption algorithm proposed in the paper has the characteristics of high security and high efficiency.

BCST-APTS: Blockchain and CP-ABE Empowered Data Supervision, Sharing, and Privacy Protection Scheme for Secure and Trusted Agricultural Product Traceability System

Blockchain provides new technologies and ideas for the construction of agricultural product traceability system (APTS). However, if data is stored, supervised, and distributed on a multiparty equal blockchain, it will face major security risks, such as data privacy leakage, unauthorized access, and trust issues. How to protect the privacy of shared data has become a key factor restricting the implementation of this technology. We propose a secure and trusted agricultural product traceability system (BCST-APTS), which is supported by blockchain and CP-ABE encryption technology. It can set access control policies through data attributes and encrypt data on the blockchain. This can not only ensure the confidentiality of the data stored in the blockchain, but also set flexible access control policies for the data. In addition, a whole-chain attribute management infrastructure has been constructed, which can provide personalized attribute encryption services. Furthermore, a reencryption scheme based on ciphertext-policy attribute encryption (RE-CP-ABE) is proposed, which can meet the needs of efficient supervision and sharing of ciphertext data. Finally, the system architecture of the BCST-APTS is designed to successfully solve the problems of mutual trust, privacy protection, fine-grained, and personalized access control between all parties.

Encryption of ciphertext data in Internet of Things based on HECRT key management

The current ciphertext data encryption system of the Internet of Things (IoT) has some problems, such as long execution time and small probability of query-shared key. This paper proposes a new ciphertext data encryption method based on HECRT key management. CPU is used to calculate the password in the special engine, which can encrypt the relationship between the engine and other modules in ciphertext data encryption system. HECRT key management is used to construct the network model of the IoT, and the Chinese residual equation is used to realise the encryption method of ciphertext data of the IoT. The experimental results show that the throughput of the proposed method is more consistent with the SQL Server database, the execution time is lower than 0.35 s, and the probability of discovering Shared keys is higher than 80%, which can prove that the proposed system has strong usability and encryption capability.

A fully homomorphism encryption scheme based on LWR

We believe that isomorphic encryption technology can provide strong technical support for users’ privacy protection in a distributed computing environment. There are three types of quasi-homomorphism encryption methods: partial homomorphism encryption, shallow homomorphism encryption, and full homomorphism encryption. homomorphism encryption methods have important applications for ciphertext data computing in distributed computing environments, such as secure cloud computing, fee computing, and remote file storage ciphertext retrieval. It is pointed out that the construction of the homomorphism encryption method is still in the theoretical stage and cannot be used for real high-density data calculation problems. How to design (natural) isomorphic encryption schemes according to algebraic systems is still a challenging research. This question discusses the problem of Learning With Rounding (LWR). Based on the difficulty of LWR, multiple IDs, and attribute categories, a fully homomorphism encryption method corresponding to an ID is proposed. In this paper, in order to reflect the effectiveness of the proposed method, we propose a homomorphism encryption technology based on the password search attribute.

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

A secure and efficient range query method for two‐layer wireless sensor networks

Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0–1 encoding method and Hash-based Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.

3D image hiding using deep demosaicking and computational integral imaging

This paper proposes a 3D image hiding algorithm with deep learning based demosaicking and computational integral imaging methods. Compared with the previous color 3D image coding method, the proposed algorithm can greatly reduce the amount of ciphertext data by two-thirds through the color filter array (CFA). And the generated CFA is coded by our proposed block cellular automata coding method, which has high security. It is worth noting that The deep learning based demosaicking algorithm can effectively resolve the problem of low resolution that exists in the conventional integral imaging reconstruction algorithms.

Full encryption

In this paper, we present a novel mechanism called Full Encryption (FE) in GaussDB. FE-in-GaussDB provides column-level encryption for sensitive data, and secures the asset from any malicious cloud administrator or information leakage attack. It ensures not only the security of operations on ciphertext data, but also the efficiency of query execution, by combining the advantages of cryptography algorithms (i.e. software mode) and Trusted Execution Environment (i.e. hardware mode). With this, FE-in-GaussDB supports full-scene query processing including the matching, the comparison and other rich computing functionalities. We demonstrate the prototype of FE-in-GaussDB and an experimental performance evaluation to prove its availability and effectiveness.

Secure File Storage On Cloud Using Hybrid Cryptography

The servers that are accessed over the web and the software and databases that run on those servers are together known as "The Cloud". The amount of data needing storage is increasing every day and thus, there is a requirement for increased storage space. Cloud allows us to do the same. Storage of data on the cloud is done for various companies, colleges, for military purposes, etc. The data on the cloud is susceptible to various risks such as lack of backup services, data leakage, lack of control over your data being stored, etc. To provide a solution to these risks over cloud storage there are several ways, which include Cryptography and Steganography. Cryptography is quite popular for data security. The Cryptography technique translates original data into an unreadable form known as ciphertext. Text is converted into an unreadable form using keys. This ensures that only an authorized entity with the right key, can access the data from the cloud server. Ciphertext data is visible for everyone. There are two types of cryptography algorithms namely Symmetric Key cryptography and Asymmetric Key Cryptography. The use of a single cryptography algorithm provides basic security. In this paper, we have suggested the use of a combination of symmetric key cryptography algorithms namely: AES-GCM, Fernet, AES-CCM, CHACHA20_POLY1305 algorithm, which help to provide high-level security to the data. Here, the key is also secured using the Fernet algorithm. The file is split into N parts. Each part is encrypted simultaneously. For the file decryption purpose reverse process of encryption is applied.

Quantum k-means algorithm based on trusted server in quantum cloud computing

Quantum k-means algorithm is widely used in solving clustering problems. However, the repeated calculation of the core subroutines overloads the quantum computer with mass computing tasks. In addition, due to the decoherence of the quantum state, the computing power of personal quantum computer is limited. Therefore, it is necessary to use high-performance quantum cloud computing to complete data processing. This paper proposes a quantum k-means algorithm in quantum cloud computing, which uploads the data to the server to execute the core subroutines SwapTest (calculating the similarity between ciphertext quantum states) and GroverOptim (quantum minimization algorithm) of quantum k-means algorithm and then sends the ciphertext to the client for further decryption using the decryption key, which reduces the load of the client. An improved quantum homomorphic encryption by t-gate updating in trusted server and data processing in semi-trusted server is introduced as well. In this scheme, the trusted server assists the semi-trusted server to execute the T-gate existing in the quantum circuit, which separates the key from the ciphertext and ensure the security of the ciphertext data processing in the quantum cloud. In our experiment, the two subroutines, SwapTest and GroverOptim, are conducted in ciphertext and plaintext conditions by using IBM Qiskit to evaluate the reliability and feasibility. The result shows that the decrypted ciphertext result is the same as the plaintext. We conclude that the proposed algorithm has a better performance in reducing the cost of load in client and protecting privacy data in cloud by contrast with the original quantum k-means algorithm.

A certificateless keyword searchable encryption scheme in multi‐user setting for fog‐enhanced Industrial Internet of Things

AbstractIndustrial Internet of Things (IIoT), an appendage of the Internet of Things (IoT), upgrades conventional industries to intelligent and autonomous industries. It integrates different intelligent terminals, sensors, actuators, mobile devices, cloud computing through wireless communication to enhance operational efficiency and safety. However, it relies on the powerful data processing and unlimited storage capabilities of cloud computing by outsourcing its data and on‐demand services to the cloud server. This introduces privacy and confidentiality issues, and a high cost of data access on IIoT, especially for location bound data retrieval. To enforce confidentiality in IIoT, encryption techniques are being adapted to secure the outsourced data, but not without impeding the data searching operation. Several searchable encryption schemes have been proposed for secure and privacy‐preserving data retrieval in IIoT. However, most of these schemes not only completely relied on the cloud server but also susceptible to inside keyword guessing attack (IKGA). Besides, some of them use high computation intensive cryptography operations that make them computationally intensive for resource‐constrained data users and owners. In this article, we develop a lightweight searchable public‐key encryption scheme. The scheme is provably secure against IKGA. It contains a secure key distribution approach for generating and distributing the symmetric encryption key and allows secure ciphertext data search and retrieval in a multi‐user setting without compromising privacy. Finally, we analyze and evaluate the performance of the proposed scheme by comparing the results with some recent state‐of‐the‐art schemes to quantitatively show the capabilities of the scheme.

Secure File Storage Using Hybrid Cryptography

To meet the required security needs of data center of cloud, blowfish used for the encryption of file slices takes minimum time and has maximum throughput for encryption and decryption from other symmetric algorithms. The idea of splitting and merging adds on to meet the principle of data security. The hybrid approach when deployed in cloud environment makes the remote server more secure and thus, helps the cloud providers to fetch more trust of their users. For data security and privacy protection issues, the fundamental challenge of separation of sensitive data and access control is fulfilled. Cryptography technique translates original data into unreadable form. Cryptography technique is divided into symmetric key cryptography and public key cryptography. This technique uses keys for translate data into unreadable form. So only authorized person can access data from cloud server. Cipher text data is visible for all people.