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In low-power hardware implementations for classification algorithms, it is often essential to use physical resources efficiently. In this sense, the use of modulo-M integer operations instead of floating-point arithmetic, can lead to better performance, especially when M represents the dynamic range of an arithmetic block of the Residue Number System (RNS) [1,2]. Following this premise, this work is aiming to provide a methodology for implementing a classifier, specifically a Support Vector Machine (SVM) [3], using modulo-M integers and proposing a method for the use of Residue Number System.

Big data becomes the key for ubiquitous computing and intelligence, and Distributed Storage Systems (DSS) are widely used in large-scale data centers or in the cloud for efficient data management. However, the data on stored are likely to be unavailable due to hardware failures and cyberattacks, e.g. DDoS. Maximum Distance Separable (MDS) codes are commonly used for the recovery of faulty storage nodes or unavailable data. However, the recovery of data nodes usually involves access to multiple nodes, which introduces significant communication overheads to the DSS. In this paper, a new DSS based on the Redundant Residue Number System (RRNS) is proposed, where efficient recovery is enabled by applying the second version of Chinese Remainder Theorem (CRT-II). The complexity and network traffic of the proposed data protection scheme is analyzed theoretically and compared with that of traditional MDS based DSSs. Experimental results show that the proposed DSS achieves lower encoding complexity, lower recovery complexity and lower network traffic than the MDS based schemes. Although the proposed data protection scheme introduces computation overheads for the case on which there are no failing nodes, its complexity is still lower for scenarios with frequent data updates. In addition, the proposed scheme introduces additional advantages in terms of security and storage flexibility.

Elliptic curve cryptography is the second most important public-key cryptography following RSA cryptography. The fundamental arithmetic of elliptic curve cryptography is a series of modular multiplications and modular additions. Usually, Montgomery algorithm is applied for modular multiplications over large integers to reduce the computational complexity. Targeting at fast elliptic curve point multiplication over prime fields a new approach in residue number system is proposed. Compared with other implementations that apply Montgomery ladder for parallel elliptic curve point multiplication, the proposed method uses a residue number system with a wide dynamic range, which supports continuous multiplications and needs only one RNS Montgomery multiplication to bring down the temporary results to valid range. Hardware implementation results demonstrate that the computation time for elliptic curve point multiplication over F p can be greatly reduced, and it takes about 0.677 ms to compute one time of elliptic curve point multiplication over 384-bit prime curves in Xilinx XC6VSX475t device, costing an area of 41409 slices, 676 DSPs and 138 Brams.

This paper presents a new design method of the reverse (residue-to-binary) converter for the flexible 3-moduli residue number system (RNS) set {2k,2n-1,2n+1}, where k and n are a pair of arbitrary integers ≥2. The basic equation of the reverse converter is formulated in two alternative forms, each of which consists of two separate parts: one depending on input variables of the converter, and the other being a single constant. The constant can be either added inside the reverse converter or shifted out to the residue datapath channels, in most cases at no hardware cost or extra delay. From the set of basic functions, which are essentially different than those of the only two known general converters proposed for this moduli set, four versions of a converter can be designed for any pair of k and n. Experimental results obtained using the commercial 65-nm low-power design kit and industrial synthesis tools for all dynamic ranges from 8 to 40 bits suggest that, compared to the state-of-the-art designs, at least one version of the newly proposed converters is superior w.r.t. delay, power consumption, and area, for all dynamic ranges considered. The savings for the best versions (these with constants moved to the datapath channels) are up to 12.7% for the area and from 2.5% to 14% (5.8 % on average) for the delay, while the power consumption is reduced up to 23.2% (5.6% on average).

PurposeThere are various system techniques or models which are used for access control by performing cryptographic operations and characterizing to provide an efficient cloud and in Internet of Things (IoT) access control. Particularly in cloud computing environment, there is a large-scale distribution of these traditional symmetric cryptographic techniques. These symmetric cryptographic techniques use the same key for encryption and decryption processes. However, during the execution of these phases, they are under the problems of key distribution and management. The purpose of this study is to provide efficient key management and key distribution in cloud computing environment.Design/methodology/approachThis paper uses the Cipher text-Policy Attribute-Based Encryption (CP-ABE) technique with proper access control policy which is used to provide the data owner’s control and share the data through encryption process in Cloud and IoT environment. The data are shared with the the help of cloud storage, even in presence of authorized users. The main method used in this research is Enhanced CP-ABE Serialization (E-CP-ABES) approach.FindingsThe results are measured by means of encryption, completion and decryption time that showed better results when compared with the existing CP-ABE technique. The comparative analysis has showed that the proposed E-CP-ABES has obtained better results of 2373 ms for completion time for 256 key lengths, whereas the existing CP-ABE has obtained 3129 ms of completion time. In addition to this, the existing Advanced Encryption Standard (AES) scheme showed 3449 ms of completion time.Originality/valueThe proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment. The novelty of the research is that the proposed E-CP-ABES incorporates extensible, partially hidden constraint policy by using a process known as serialization procedure and it serializes to a byte stream. Redundant residue number system is considered to remove errors that occur during the processing of bits or data obtained from the serialization. The data stream is recovered using the Deserialization process.

Fog computing provides a suitable development for real-time processing in the Internet of Things (IoT). Attribute-based encryption (ABE) is the main method to control data access in fog computing. A residue number system (RNS) can speed up multiplication and exponential operations by converting very large integers to small integers. This paper proposes a fine-grained lightweight access control scheme using ABE modified with RNS properties (RNS-ABE) with fog computing. Decryption is implemented with the Chinese remainder theorem (CRT), and a new access structure based on the CRT secret sharing scheme is also introduced. Security of the proposed scheme proved based on RNS properties and the complicated problem of factoring a very large integer into its large prime factors, like RSA encryption. The time cost comparison shows that the total encryption and decryption time of our scheme is more efficient than the lightweight schemes with the underlying operation of bilinear pairing.

There has been a tendency to use the theory of finite Galois fields, or GF(2n), in cryptographic ciphers (AES, Kuznyechik) and digital signal processing (DSP) systems. It is advisable to use modular codes of the polynomial residue number system (PRNS). Modular codes of PRNS are arithmetic codes in which addition, subtraction and multiplication operations are performed in parallel on the bases of the code, which are irreducible polynomials. In this case, the operands are small-bit residues. However, the independence of calculations on the bases of the code and the lack of data exchange between the residues can serve as the basis for constructing codes of PRNS capable of detecting and correcting errors that occur during calculations. The article will consider the principles of constructing redundant codes of the polynomial residue number system. The results of the study of codes of PRNS with minimal redundancy are presented. It is shown that these codes are only able to detect an error in the code combination of PRNS. It is proposed to use two control bases, the use of which allows us to correct an error in any residue of the code combination, in order to increase the error-correction abilities of the code of the polynomial residue number system. Therefore, the development of an algorithm for detecting and correcting errors in the code of the polynomial residue number system, which allows for performing this procedure based on modular operations that are effectively implemented in codes of PRNS, is an urgent task.

This paper presents a new encryption technique, which combines affine ciphers and the residue number system. This makes it possible to eliminate the shortcomings and vulnerabilities of affine ciphers, which are sensitive to cryptanalysis, using the advantages of the residue number system, i.e., the parallelization of calculation processes, performing operations on low bit numbers, and the linear combination of encrypted residues. A mathematical apparatus and a graphic scheme of affine encryption using the residue number system is developed, and a corresponding example is given. Special cases of affine ciphers such as shift and linear ciphers are considered. The cryptographic strength of the proposed cryptosystem when the moduli are prime numbers is estimated, and an example of its estimation is given. The number of bits and the number of moduli of the residue number system, which ensure the same cryptographic strength as the longest key of the AES algorithm, are determined.

Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. Due to the sensitive nature of these datasets, secure deep learning (DL) algorithms are essential. Although, fully homomorphic encryption (FHE)-based methods have been proposed to maintain data confidentiality and privacy, however, existing FHE deep convolutional neural network (CNN) models still face some issues such as low accuracy, high encryption/decryption latency, energy inefficiency, long feature extraction times, and significant cipher-image expansion. To address these issues, this study introduces the HoRNS-CNN model, which integrates the energy-efficient features of the residue number system FHE scheme (RNS-FHE scheme) with the high accuracy of pre-trained deep CNN models in the cloud for efficient, privacy-preserving predictions and provide some proofs of its energy efficiency and homomorphism. The RNS-FHE scheme's FPGA implementation includes embedded RNS pixel-bitstream homomorphic encoder/decoder circuits for encrypting 8-bit grayscale pixels, with cloud CNN models performing remote classification on the encrypted images. In the HoRNS-CNN architecture, the ReLU activation functions of deep CNNs were initially trained for stability and later adapted for homomorphic computations using a Taylor polynomial approximation of degree 3 and batch normalization to achieve high accuracy. The findings show that the HoRNS-CNN model effectively manages cipher-image expansion with an asymptotic complexity of O n 3 , offering better performance and faster feature extraction compared to its peers. The model can predict 400,000 neural-biomarker features in one hour, providing an effective tool for analyzing neuroimages while ensuring privacy and security.

Properties of redundant residue number system (RRNS) are used for detecting and correcting errors during the data storing, processing and transmission. However, detection and correction of a single error require significant decoding time due to the iterative calculations needed to locate the error. In this paper, we provide a performance evaluation of Asmuth-Bloom and Mignotte secret sharing schemes with three different mechanisms for error detecting and correcting: Projection, Syndrome, and AR-RRNS. We consider the best scenario when no error occurs and worst-case scenario, when error detection needs the longest time. When examining the overall coding/decoding performance based on real data, we show that AR-RRNS method outperforms Projection and Syndrome by 68% and 52% in the worst-case scenario.