Biblio

Pairing is carried out by two steps, Miller loop and final exponentiation. In this manuscript, the authors propose an efficient Miller loop for a pairing on the FK12 curve. A Hamming weight and bit-length of loop parameter have a great effect on the computational cost of Miller loop. Optimal-ate pairing is used as the most efficient pairing on the FK12 curve currently. The loop parameter of optimal-ate pairing is 6z+2 where z is the integer to make the FK12 curve parameter. Our method uses z which has a shorter bit-length than the previous optimal-ate pairing as the loop parameter. Usually, z has a low Hamming weight to make final exponentiation efficient. Therefore, the loop parameter in our method has a lower Hamming weight than the loop parameter of the previous one in many cases. The authors evaluate our method by the number of multiplications and execution time. As a result, the proposed algorithm leads to the 3.71% reduction in the number of multiplications and the 3.38% reduction in the execution time.

Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.

This research investigates efficient architectures for the implementation of the CRYSTALS-Dilithium post-quantum digital signature scheme on reconfigurable hardware, in terms of speed, memory usage, power consumption and resource utilisation. Post quantum digital signature schemes involve a significant computational effort, making efficient hardware accelerators an important contributor to future adoption of schemes. This is work in progress, comprising the establishment of a comprehensive test environment for operational profiling, and the investigation of the use of novel architectures to achieve optimal performance.

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.

A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT’90, SICOMP’91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness of learning programs. In this paper, we overcome Ko’s relativization barrier and prove NP-hardness of learning programs under randomized polynomial-time many-one reductions. Our result is provably non-relativizing, and comes somewhat close to the parameter range of improper learning: We observe that mildly improving our inapproximability factor is sufficient to exclude Heuristica, i.e., show the equivalence between average-case and worst-case complexities of N P. We also make progress on another long-standing open question of showing NP-hardness of the Minimum Circuit Size Problem (MCSP). We prove NP-hardness of the partial function variant of MCSP as well as other meta-computational problems, such as the problems MKTP* and MINKT* of computing the time-bounded Kolmogorov complexity of a given partial string, under randomized polynomial-time reductions. Our proofs are algorithmic information (a.k. a. Kolmogorov complexity) theoretic. We utilize black-box pseudorandom generator constructions, such as the Nisan-Wigderson generator, as a one-time encryption scheme secure against a program which “does not know” a random function. Our key technical contribution is to quantify the “knowledge” of a program by using conditional Kolmogorov complexity and show that no small program can know many random functions.

Secured data transmission between one to many authorized users is achieved through Broadcast Encryption (BE). In BE, the source transmits encrypted data to multiple registered users who already have their decrypting keys. The Untrustworthy users, known as Traitors, can give out their secret keys to a hacker to form a pirate decoding system to decrypt the original message on the sly. The process of detecting the traitors is known as Traitor Tracing in cryptography. This paper presents a new Black Box Tracing method that is fully collusion resistant and it is designated as Traitor Tracing in Broadcast Encryption using Vector Keys (TTBE-VK). The proposed method uses integer vectors in the finite field Zp as encryption/decryption/tracing keys, reducing the computational cost compared to the existing methods.

With the advent of cloud storage services many users tend to store their data in the cloud to save storage cost. However, this has lead to many security concerns, and one of the most important ones is ensuring data integrity. Public verification schemes are able to employ a third party auditor to perform data auditing on behalf of the user. But most public verification schemes are vulnerable to procrastinating auditors who may not perform auditing on time. These schemes do not have fair arbitration also, i.e. they lack a way to punish the malicious Cloud Service Provider (CSP) and compensate user whose data has been corrupted. On the other hand, CSP might be storing redundant data that could increase the storage cost for the CSP and computational cost of data auditing for the user. In this paper, we propose a Blockchain-based public auditing and deduplication scheme with a fair arbitration system against procrastinating auditors. The key idea requires auditors to record each verification using smart contract and store the result into a Blockchain as a transaction. Our scheme can detect and punish the procrastinating auditors and compensate users in the case of any data loss. Additionally, our scheme can detect and delete duplicate data that improve storage utilization and reduce the computational cost of data verification. Experimental evaluation demonstrates that our scheme is provably secure and does not incur overhead compared to the existing public auditing techniques while offering an additional feature of verifying the auditor’s performance.

Demand response has emerged as one of the most promising methods for the deployment of sustainable energy systems. Attempts to democratize demand response and establish programs for residential consumers have run into scalability issues and risks of leaking sensitive consumer data. In this work, we propose a privacy-friendly, incentive-based demand response market, where consumers offer their flexibility to utilities in exchange for a financial compensation. Consumers submit encrypted offer which are aggregated using Computation Over Encrypted Data to ensure consumer privacy and the scalability of the approach. The optimal allocation of flexibility is then determined via double-auctions, along with the optimal consumption schedule for the users with respect to the day-ahead electricity prices, thus also shielding participants from high electricity prices. A case study is presented to show the effectiveness of the proposed approach.

In this paper, we present a flexible Variable Precision Computation Array (VPCA) component for different accelerators, which leverages a sparsification scheme for activations and a low bits serial-parallel combination computation unit for improving the efficiency and resiliency of accelerators. The VPCA can dynamically decompose the width of activation/weights (from 32bit to 3bit in different accelerators) into 2-bits serial computation units while the 2bits computing units can be combined in parallel computing for high throughput. We propose an on-the-fly compressing and calculating strategy SLE-CLC (single lane encoding, cross lane calculation), which could further improve performance of 2-bit parallel computing. The experiments results on image classification datasets show VPCA can outperforms DaDianNao, Stripes, Loom-2bit by 4.67×, 2.42×, 1.52× without other overhead on convolution layers.

Differential evolution algorithm is an efficient computational method that uses population crossover and variation to achieve high-quality solutions. The algorithm is simple in principle and fast in solving global solutions, so it has been widely used in complex optimization problems. In this paper, we applied the differential evolution algorithm to a time-delay dynamic system for microbial fermentation of 1,3-propanediol and obtained an average error of 22.67% comparing to baseline error of 48.53%.

Mobile devices have been widely used to deploy security-sensitive applications such as mobile payments, mobile offices etc. SM2 digital signature technology is critical in these applications to provide the protection including identity authentication, data integrity, action non-repudiation. Since mobile devices are prone to being stolen or lost, several server-aided SM2 cooperative signature schemes have been proposed for the mobile scenario. However, existing solutions could not well fit the high-concurrency scenario which needs lightweight computation and communication complexity, especially for the server sides. In this paper, we propose a SM2 cooperative signature algorithm (SM2-CSA) for the high-concurrency scenario, which involves only one-time client-server interaction and one elliptic curve addition operation on the server side in the signing procedure. Theoretical analysis and practical tests shows that SM2-CSA can provide better computation and communication efficiency compared with existing schemes without compromising the security.

Payment channel hubs (PCHs) constitute a promising solution to the inherent scalability problem of blockchain technologies, allowing for off-chain payments between sender and receiver through an intermediary, called the tumbler. While state-of-the-art PCHs provide security and privacy guarantees against a malicious tumbler, they do so by relying on the scripting-based functionality available only at few cryptocurrencies, and they thus fall short of fundamental properties such as backwards compatibility and efficiency.In this work, we present the first PCH protocol to achieve all aforementioned properties. Our PCH builds upon A2L, a novel cryptographic primitive that realizes a three-party protocol for conditional transactions, where the tumbler pays the receiver only if the latter solves a cryptographic challenge with the help of the sender, which implies the sender has paid the tumbler. We prove the security and privacy guarantees of A2L (which carry over to our PCH construction) in the Universal Composability framework and present a provably secure instantiation based on adaptor signatures and randomizable puzzles. We implemented A2L and compared it to TumbleBit, the state-of-the-art Bitcoin-compatible PCH. Asymptotically, A2L has a communication complexity that is constant, as opposed to linear in the security parameter like in TumbleBit. In practice, A2L requires 33x less bandwidth than TumleBit, while retaining the computational cost (or providing 2x speedup with a preprocessing technique). This demonstrates that A2L (and thus our PCH construction) is ready to be deployed today.In theory, we demonstrate for the first time that it is possible to design a secure and privacy-preserving PCH while requiring only digital signatures and timelock functionality from the underlying scripting language. In practice, this result makes our PCH backwards compatible with virtually all cryptocurrencies available today, even those offering a highly restricted form of scripting language such as Ripple or Stellar. The practical appealing of our construction has resulted in a proof-of-concept implementation in the COMIT Network, a blockchain technology focused on cross-currency payments.

The burgeoning networked computing devices create many distributed systems and generate new signals on a large scale. Many Internet of Things (IoT) applications, such as peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks, can be modeled as a form of big data. Processing massive data calls for new data structures and algorithms different from traditional ones designed for small-scale problems. For measurement from networked distributed systems, we consider an essential data format: signals on graphs. Due to limited computing resources, the sensor nodes in the distributed systems may outsource the computing tasks to third parties, such as cloud platforms, arising a severe concern on data privacy. A de-facto solution is to have third parties only process encrypted data. We propose a novel and efficient privacy-preserving secure outsourced computation protocol for denoising signals on the graph based on the information-theoretic secure multi-party computation (ITS-MPC). Denoising the data makes paths for further meaningful data processing. From experimenting with our algorithms in a testbed, the results indicate a better efficiency of our approach than a counterpart approach with computational security.

Fully homomorphic encryption (FHE) provides effective security assurance for privacy computing in cloud environments. But the existing FHE schemes are generally faced with challenges including using single-bit encryption and large ciphertext noise, which greatly affects the encryption efficiency and practicability. In this paper, a low-noise FHE scheme supporting multi-bit encryption is proposed based on the HAO scheme. The new scheme redesigns the encryption method without changing the system parameters and expands the plaintext space to support the encryption of integer matrices. In the process of noise reduction, we introduce a PNR method and use the subGaussian distribution theory to analyze the ciphertext noise. The security and the efficiency analysis show that the improved scheme can resist the chosen plaintext attack and effectively reduce the noise expansion rate. Comparative experiments show that the scheme has high encryption efficiency and is suitable for the privacy-preserving computation of integer matrices.

Cryptographic algorithms are playing an important role in the information security field. Strong and unbreakable algorithms provide high security and good throughput. The strength of any encryption algorithm is basically based on the degree of difficulty to obtain the encryption key by such cyber-attacks as brute. It is supposed that the bigger the key size, the more difficult it is to compute the key. But increasing the key size will increase both the computational complexity and the processing time of algorithms. In this paper, we proposed a reliable, effective, and more secure symmetric stream cipher algorithm for encryption and decryption called Symmetric Cipher based on Key Hashing Algorithm (SCKHA). The idea of this algorithm is based on hashing and splitting the encryption symmetric key. Hashing the key will hide the encrypted key to prevent any intruder from forging the hash code, and, thus, it satisfies the purpose of security, authentication, and integrity for a message on the network. In addition, the algorithm is secure against a brute-force attack by increasing the resources it takes for testing each possible key. Splitting the hashed value of the encryption key will divide the hashed key into two key chunks. The encryption process performed using such one chunk based on some calculations on the plaintext. This algorithm has three advantages that are represented in computational simplicity, security and efficiency. Our algorithm is characterized by its ability to search on the encrypted data where the plaintext character is represented by two ciphertext characters (symbols).

Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.

Cloud based cyber physical system (CPS) enables individuals to store and share data collected from both cyberspace and the physical world. This leads to the proliferation of massive data at a user's local site. Since local storage systems can't store and maintain huge data, it is a wise and practical way to outsource such huge data to the cloud. Cloud storage provides scalable storage space to manage data economically and flexibly. However, the integrity of outsourced data is a critical challenge because user's lose control of their data once it's transferred to cloud servers. Several auditing schemes have been put forward based on public key infrastructure (PKI) or identity-based cryptography to verify data integrity. However, “the PKI-based schemes suffer from certificate management problem and identity-based schemes face the key escrow” problem. Therefore, to address these problems, certificateless public auditing schemes have been introduced on the basis of bilinear pairing, which incur high computation overhead, and thus it is not suitable for CPS. To reduce the computation overhead, in this paper, Using elliptic curve cryptography, we propose an efficient pairing-free certificateless public auditing scheme for cloud-based CPS. The proposed scheme is more secure against type I/II/III adversaries and efficient compared to other certificateless based schemes.

Recently, with the development of the cloud environment, users can store their data or share it with other users. However, various security threats can occur in data sharing systems in the cloud environment. To solve this, data sharing systems and access control methods using the CP-ABE method are being studied, but the following problems may occur. First, in an outsourcing server that supports computation, it is not possible to prove that the computed result is a properly computed result when performing the partial decryption process of the ciphertext. Therefore, the user needs to verify the message obtained by performing the decryption process, and verify that the data is uploaded by the data owner through verification. As another problem, because the data owner encrypts data with attribute-based encryption, the number of attributes included in the access structure increases. This increases the size of the ciphertext, which can waste space in cloud storage. Therefore, a ciphertext of a constant size must be output regardless of the number of attributes when generating the ciphertext. In this paper, we proposes a CP-ABE based data sharing system that provides signature-based verifiable outsourcing. It aims at a system that allows multiple users to share data safely and efficiently in a cloud environment by satisfying verifiable outsourcing and constant-sized ciphertext output among various security requirements required by CP-ABE.

In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding with ambiguation (SCA) mechanism that introduces the notion of inherent shared secrecy based on the support intersection of sparse codes. This approach is ‘fairness-aware’, in the sense that any point in the neighborhood has an equiprobable chance to be chosen. Our approach can be applied to raw data, latent representation of autoencoders, and aggregated local descriptors. The proposed method is tested on both synthetic i.i.d data and real image databases.

K-nearest neighbor algorithm that searches the K closest samples in a high dimensional feature space is one of the most fundamental tasks in machine learning and image retrieval applications. Computational storage device that combines computing unit and storage module on a single board becomes popular to address the data bandwidth bottleneck of the conventional computing system. In this paper, we propose a nearest neighbor search acceleration platform based on computational storage device, which can process a large-scale image dataset efficiently in terms of speed, energy, and cost. We believe that the proposed acceleration platform is promising to be deployed in cloud datacenters for data-intensive applications.

Vehicular ad hoc networks (VANETs) ensure improvement in road safety and traffic management by allowing the vehicles and infrastructure that are connected to them to exchange safety messages. Due to the open wireless communication channels, security and privacy issues are a major concern in VANETs. A typical attack consists of a malicious third party intercepting, modifying and retransmitting messages. Heterogeneous vehicular communication in VANETs occurs when vehicles (only) or vehicles and other infrastructure communicate using different cryptographic techniques. To address the security and privacy issues in heterogeneous vehicular communication, some heterogeneous signcryption schemes have been proposed. These schemes simultaneously satisfy the confidentiality, authentication, integrity and non-repudiation security requirements. They however fail to properly address the efficiency with respect to the computational cost involved in unsigncrypting ciphertexts, which is often affected by the speeds at which vehicles travel in VANETs. In this paper, we propose an efficient conditional privacy-preserving hybrid signcryption (CPP-HSC) scheme that uses bilinear pairing to satisfy the security requirements of heterogeneous vehicular communication in a single logical step. Our scheme ensures the transmission of a message from a vehicle with a background of an identity-based cryptosystem (IBC) to a receiver with a background of a public-key infrastructure (PKI). Furthermore, it supports a batch unsigncryption method, which allows the receiver to speed up the process by processing multiple messages simultaneously. The security of our CPP-HSC scheme ensures the indistinguishability against adaptive chosen ciphertext attack (IND-CCA2) under the intractability assumption of q-bilinear Diffie-Hellman inversion (q-BDHI) problem and the existential unforgeability against adaptive chosen message attack (EUF-CMA) under the intractability assumption of q-strong Diffie-Hellman (q-SDH) problem in the random oracle model (ROM). The performance analysis indicates that our scheme has an improvement over the existing related schemes with respect to the computational cost without an increase in the communication cost.

Using erasure codes increases consumption of network traffic and disk I/O tremendously when systems recover data, resulting in high latency of degraded reads. In order to mitigate this problem, we present an adaptive storage scheme based on data access skew, a fact that most data accesses are applied in a small fraction of data. In this scheme, we use both Local Reconstruction Code (LRC), whose recovery cost is low, to store frequently accessed data, and Hitchhiker (HH) code, which guarantees minimum storage cost, to store infrequently accessed data. Besides, an efficient switching algorithm between LRC and HH code with low network and computation costs is provided. The whole system will benefit from low degraded read latency while keeping a low storage overhead, and code-switching will not become a bottleneck.

Privacy-preserving frequency computation is critical to privacy-preserving data mining in 2-Part Fully Distributed Setting (such as association rule analysis, clustering, and classification analysis) and has been investigated in many researches. However, these solutions are based on the Elgamal Cryptosystem, making computation and communication efficiency low. Therefore, this paper proposes an improved protocol using an Elliptic Curve Cryptosystem. The theoretical and experimental analysis shows that the proposed method is effective in both computing and communication compared to other methods.

With the shift in storage paradigm, there is an increasing need for privacy of dataset and also for an encryption scheme that permits computation on encrypted data. Paillier cryptosystem is a good example of such a homomorphic encryption scheme. To improve the efficiency of the Paillier homomorphic encryption scheme in terms of its decryption speed and overall computational cost, we propose an improved decryption process. Specifically, the inclusion of a variable k to reduce the modular multiplicative arithmetic. The variable k is combined with the L function and CRT recombination method, to arrive at a fast and improved decryption process, showing the mathematical correctness of the decryption algorithm. Experimental results validate that our scheme is significantly efficient in its decryption speed.

As a collection of innovative technologies, blockchain has solved the problem of reliable transmission and exchange of information on untrusted networks. The underlying implementation is the basis for the reliability of blockchain, which consists of various cryptographic algorithms for the use of identity authentication and privacy protection of distributed ledgers. The cryptographic algorithm plays a vital role in the blockchain, which guarantees the confidentiality, integrity, verifiability and non-repudiation of the blockchain. In order to get the most suitable cryptographic algorithm for the blockchain system, this paper proposed a method using Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and score the comprehensive performance of the three types of cryptographic algorithms applied in the blockchain, including symmetric cryptographic algorithms, asymmetric cryptographic algorithms and hash algorithms. This paper weighs the performance differences of cryptographic algorithms considering the aspects of security, operational efficiency, language and hardware support and resource consumption. Finally, three cryptographic algorithms are selected that are considered to be the most suitable ones for block-chain systems, namely ECDSA, sha256 and AES. This result is also consistent with the most commonly used cryptographic algorithms in the current blockchain development direction. Therefore, the reliability and practicability of the algorithm evaluation pro-posed in this paper has been proved.