Biometric authentication is adopted in many access control scenarios in recent years. It is very convenient and secure since it compares the user’s own biometrics with those stored in the database to confirm their identification. Since then, with the vigorous development of machine learning, the performance and accuracy of biometric authentication have been greatly improved. Face recognition technology combined with convolutional neural network (CNN) is extremely efficient and has become the mainstream of access control systems (ACSs). However, identity information and access logs stored in traditional databases can be tampered by malicious insiders. Therefore, we propose a face recognition ACS that is resistant to data forgery. In this paper, a deep convolutional network is utilized to learn Euclidean embedding (based on FaceNet) of each image and achieve face recognition and verification. Quorum, which is built on the Ethereum blockchain, is used to store facial feature vectors and login information. Smart contracts are made to automatically put data into blocks on the chain. One is used to store feature vectors, and the other to record the arrival and departure times of employees. By combining these cutting‐edge technologies, an intelligent and immutable ACS that can withstand distributed denial‐of‐service (DDoS) and other internal and external attacks is created. Finally, an experiment is conducted to assess the effectiveness of the proposed system to demonstrate its practicality.
Password-based authentication is widely applied in Internet of Things (IoT). It allows IoT devices to identify users with passwords to resist unauthorized access. However, choices of weak passwords, especially popular ones, might violate users’ privacy and lead to large-scale network attacks. Collection of popular passwords among IoT devices to establish blocklists via a service provider can prevent use of weak passwords. To protect unpopular passwords during collection, existing privacy-preserving schemes rely on expensive cryptographic primitives (e.g., garbled circuits and zero-knowledge proofs), which would impose heavy communication and computation burdens on constrained devices and hinder wide deployment of these schemes. In this paper, we propose EAGER+, an efficient privacy-preserving scheme for weak password collection in IoT against perpetual leakage. EAGER+ is mainly built on secret sharing and symmetric encryption, thereby enabling lightweight computation and communication on IoT devices. In EAGER+, we conceive a password-locked encryption with conditional decryption mechanism to efficiently identify popular passwords, where a password is essentially locked under itself in the encryption to guarantee its security, and the password can be revealed from the ciphertext by the service provider only if a sufficient number of devices exploit it. The mechanism is integrated with a servers-aided password-hardening mechanism to resist offline dictionary guessing attacks. Moreover, EAGER+ uses a key renewal mechanism to periodically update secrets for password hardening on key servers to thwart perpetual leakage towards the secrets. We formally analyze the security of EAGER+, and conduct experimental evaluations to show that EAGER+ is more efficient than existing schemes.
Decentralized identity systems promise user-controlled identifiers and cross-domain verification without a shared identity provider, yet authentication still reduces to possession of keys or credentials once secrets are leaked, reused, or replayed. We present BioZero, a privacy-preserving biometric authentication protocol for decentralized identity that binds an enrolled identity to a biometric witness without revealing biometric templates, while enabling publicly verifiable on-chain decisions. BioZero combines Pedersen commitment-homomorphic computation, consistency spot-checks, and Groth16 zero-knowledge proofs to achieve identity-bound authentication with succinct on-chain verification. We analyze acceptance soundness, freshness, template privacy, and non-malleability under an open decentralized threat model including replay, timing, brute-force, oracle, and forgery attacks. On an Ethereum testbed, BioZero achieves up to 67.8x lower network-adjusted total authentication latency and up to 266.4x faster client-side proving than a zk-SNARK-only baseline. Verification stays in the millisecond range (28.8-41.2 ms vs. 35.4-77.6 ms). With lambda=1 spot-checking, gas grows from 336,778 to 954,066 as N increases from 2 to 128, becomes lower than the baseline from N>=16, and is 2.59x lower at N=128. LFW experiments on 128D and 512D models show accuracy loss below 1% across practical quantization ranges. These results indicate that BioZero is a practical authentication layer for decentralized biometric identity systems.
The increased use of artificial intelligence generated content (AIGC) among vast user populations has heightened the risk of private data leaks. Effective auditing and regulation remain challenging, further compounding the risks associated with the leaks involving model parameters and user data. Blockchain technology, renowned for its decentralized consensus mechanism and tamper-resistant properties, is emerging as an ideal tool for documenting, auditing, and analyzing the behaviors of all stakeholders in machine learning as a service (MLaaS). This study centers on biometric recognition systems, addressing pressing privacy and security concerns through innovative endeavors. We conducted experiments to analyze six distinct deep neural networks, leveraging a dataset quality metric grounded in the query output space to quantify the value of the transfer datasets. This analysis revealed the impact of imbalanced datasets on training accuracy, thereby bolstering the system's capacity to detect model data thefts. Furthermore, we designed and implemented a novel Bio-Rollup scheme, seamlessly integrating technologies such as certificate authority, blockchain layer two scaling, and zero-knowledge proofs. This innovative scheme facilitates lightweight auditing through Merkle proofs, enhancing efficiency while minimizing blockchain storage requirements. Compared to the baseline approach, Bio-Rollup restores the integrity of the biometric system and simplifies deployment procedures. It effectively prevents unauthorized use through certificate authorization and zero-knowledge proofs, thus safeguarding user privacy and offering a passive defense against model stealing attacks.
Alec Wells, Norbert Dajnowski, Aminu Bello Usman, John Murray · 5 authors
This paper presents a novel framework for one-to-many biometric systems by adapting decentralised storage over a centralised database solution, by leveraging smart contracts to address the concerns commonly associated with decentralised solutions. Smart contracts enforce strict privacy controls, enabling individuals to retain ownership and control over their biometric data on decentralised networks, while facilitating secure and efficient authentication, helping achieve the principles laid out by privacy by design. Biometric systems play a crucial role in identity verification and access control, but their deployment raises significant privacy challenges due to the sensitive nature of biometric data. Traditional approaches often involve centralised storage of biometric information, increasing the risk of data breaches and unauthorised access. We discuss the architecture, implementation, and benefits of our framework, highlighting its potential to enhance privacy and trust in one-to-many biometric systems across various applications.
Samuel Hand, Alexander Koch, Pascal Lafourcade, Daiki Miyahara · 5 authors
Abstract A zero-knowledge proof (ZKP) allows a prover to prove to a verifier that it knows some secret, such as a solution to a difficult puzzle, without revealing any information about it. In recent years, ZKP protocols using only a deck of playing cards for solutions to various pencil puzzles have been proposed. The previous work of Lafourcade et al. deals with a famous puzzle called Slitherlink. Their proposed protocol can verify that a solution forms a single loop without revealing anything about the solution, except this fact. Their protocol guarantees that the solution satisfies the single-loop condition, by interactively constructing a solution starting from a state that holds a simple single loop, and proceeding via steps that preserve the invariant of encoding a single loop, until the proper solution is reached. A drawback of their protocol is that it requires additional verifications to guarantee a single loop. In this study, we propose a more efficient ZKP protocol for such a puzzle with fewer additional verifications. For this, we employ the previous work of Robert et al., which addressed the connectivity property in a puzzle. That is, we verify that a solution is connected but not split, to be a single loop. Applying our proposal, we construct a card-based ZKP protocol for Moon-or-Sun, which has its specific rule of alternating pattern in addition to the single-loop condition.
Open access
Advanced Steganography and Watermarking Techniques
Chunjie Guo, Lin You, Xingyu Li, Gengran Hu · 6 authors
Biometric authentication is a very convenient and user-friendly method. The popularity of this method requires strong privacy-preserving technology to prevent the disclosure of template information. Most of the existing privacy protection technologies rely on classic encryption techniques, such as homomorphic encryption, which incur huge system overhead and cannot be popularized. To address these issues, we propose a novel biometric authentication scheme with privacy protection based on support vector machine and zero knowledge proof (BioAu–SVM+ZKP). BioAu–SVM+ZKP allows users to authenticate themselves to different service providers without disclosing any biometric template information. The evidence is generated through the zero-knowledge proof utilizing polynomial commitments. Our approach for generating a unique and repeatable biometric identifier from the user’s fingerprint image leverages the multi-classification property of SVM. Notably, our scheme not only reduces the communication overhead but also provides the privacy protection features. Besides, the communication overhead of BioAu–SVM+ZKP is constant. We have simulated the authentication scheme on the common dataset NIST, analyzed the performance and proved the security.
Open access
Biometric Identification and Security
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques
Biometric authentication has been used in applications in various environments as a secure authentication method in computing systems.When combined with blockchain technology, the security of the biometric authentication system can be further enhanced.In this paper, we propose a biometric authentication system that does not expose the original biometric information during the user's biometric authentication process by utilizing a fully homomorphic encryption.In addition, our proposed authentication system utilizes Ethereum's smart contract, which is one of the most famous public blockchains, to record the authentication log between the user and the service provider in a distributed ledger to enhance accountability and traceability.The system is designed to be used only after obtaining the consent of the biometric data subject(user) to comply with the privacy law represented by the European General Data Protection Regulation (GDPR).Finally, we show that the proposed system can process biometric information while maintaining confidentiality, integrity, and accountability of users via security analysis.The cost of maintaining the proposed system is acceptable by analyzing computation time and blockchain maintenance cost.
Saeed Bamashmos, Naveen Chilamkurti, Ahmad Salehi Shahraki
Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.
Open access
User Authentication and Security Systems
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
Muhammad Ahmad Nawaz Ul Ghani, Kun She, Muhammad Arslan Rauf, Masoud Alajmi · 6 authors
In recent years, facial recognition technology has become increasingly integrated into society, making privacy protection crucial. Previous techniques offered minimal secrecy safeguards through simple obscuration methods. This paper addresses the strict privacy requirements of face image data by developing a novel framework that synergistically integrates Generative Adversarial Networks (GANs), clustering algorithms, and Blockchain technology. The methodology proposes a cutting-edge Privacy-Preserving Self-Attention GAN (PPSA-GAN) to generate realistic synthetic facial imagery. An integrated mini-batch K-means clustering algorithm anonymizes these images into distinct groupings, maximizing privacy preservation. Blockchain integration complements the system by fortifying trust through decentralized ledgers for transparent yet secure data storage and auditing. Rigorous benchmarking on the CelebA dataset confirms the PPSA-GAN architecture’s state-of-the-art performance, attaining an impressive Inception Score of 13.99 and a Fréchet Inception Distance of 35.50. The mini-batch clustering forms 125 distinct clusters, effectively anonymizing facial attributes within the synthetic images. Blockchain integration further bolsters privacy assurances via tamper-proof historical records, showcasing precision, recall, F1-score, and accuracy values of 0.948, 0.938, 0.943, and 0.947, respectively. This multifunctional framework represents a novel contribution, fostering an ethical technological ecosystem that balances progress and privacy. Prospective deployment horizons encompass identity verification, surveillance infrastructure, and augmentation of medical image repositories, seeding an enlightening future for facial recognition domains.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.
Abstract Blockchain technology has become an emerging area in recent years due to its capacity to improve the security, dependability, and resilience of distributed systems. Research based on this technique has impacted several firms, including banking, healthcare, data processing, remote sensing, and many others. The key characteristics of blockchain technology that make it appealing are data immutability, transparency, privacy, decentralization, and distributed ledgers. However, there is a chance of a privacy breach with sensitive biometric data. The purpose of this investigation is to examine blockchain‐based biometric applications research. It begins by determining the myriad ways that biometrics and blockchain may work together, including the storage and protection of biometric templates, identity management, and biometric authentication systems. Different biometric applications with respect to blockchain technology are also identified, along with the types of biometric data taken into account, features and capabilities of blockchain technology exploited, and blockchain technology frameworks employed. Finally, the authors seek to investigate blockchain concepts in the biometric domain by evaluating their pros and cons and summarizing the methods developed on blockchain for diverse biometric applications. Additionally, the applications of blockchain‐based biometric systems are highlighted before moving on to open research questions and potential future research areas.
In the rapidly evolving environment of wireless medical sensor networks (WMSN) and the internet of medical things (IoMT), remote medical support has seen unprecedented advancements. It is essential that the data relayed from the sensors must be trustworthy and unaltered, and that the sensors themselves are genuine. Wireless networks, however, have inherent vulnerabilities. In addition, since WMSN is directly linked to patients’ lives, its continuous availability is crucial. Considerable efforts have been made to maintain the integrity and authenticity of such data. However, many studies have failed to address the problem of a single point of failure (SPOF). This issue has been particularly detrimental to patients who require ongoing management. To address this issue and ensure the protection of the authenticity and integrity of patient data, we suggest the implementation of an authentication scheme based on blockchain technology. In 2022, Yu et al. introduced a blockchain-integrated authentication and key generation scheme for WMSN using Physical Unclonable Functions (PUFs), effectively addressing the SPOF problem by conducting mutual authentication through smart contracts without relying on centralized servers. Our research found that this scheme inadvertently shared critical parameters, including challenge-response pairs and important private keys, with the blockchain network, making it vulnerable to various breaches. We present an enhanced protocol designed to mitigate these security challenges. By limiting the data interaction with smart contracts and ensuring only relevant parties access crucial parameters, our approach reduces the risk of public information disclosure on the blockchain. This not only mitigates the SPOF issue but also efficiently helps in prevention of physical attacks. We prove that our proposed system prevents known security vulnerabilities through informal and formal analysis using the Scyther, Proverif, and BAN logic. Furthermore, the proposed scheme offers 67.37% reduction in computation costs and 3.67% in communication costs, presenting an efficient and secure solution for WMSN in the IoMT landscape.
Abstract In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate.
In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management.
Dato Kavazi, Victor Smirnov, Sasha Shilina, Jonathan Shomroni · 7 authors
We present a novel 1 Human = 1 Node blockchain protocol which aims to overcome problems arising from plutocratic principles upon which Proof-of-Work (PoW) and Proof-of-Stake (PoS) heavily rely on. The advent of blockchain technology has led to a massive wave of different decentralized ledger technology (DLT) solutions. Projects such as Bitcoin and Ethereum managed to shift the paradigm of how to transact value in a decentralized manner, yet their core technologies give rise to a significant early adopters’ control bias and have led to financial systems flawed by massive inequality and centralization of power. In this paper we propose an alternative to modern decentralized financial networks by introducing the Humanode network. Humanode is a network safeguarded by cryptographically secure bio-authorized nodes on which users are able to deploy nodes by staking their encrypted biometric data. This approach can potentially lead to the creation of a truly public, permissionless financial network, based on consensus between equal human nodes with algorithmic emission mechanisms targeting real value growth and contribution-based wealth distribution.
With the advent of the era of big data, privacy computing analyzes and calculates data on the premise of protecting data privacy, to achieve data 'available and invisible'. As an important branch of secure multi-party computation, the geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, and has important research significance. In this paper, we study the similarity problem of geometric graphics. First, this paper proposes the adjacency matrix vector coding method of isomorphic graphics, and use the Paillier variant encryption cryptography to solve the problem of isomorphic graphics confidentiality under the semi-honest model. Using cryptography tools such as elliptic curve cryptosystem, zero-knowledge proof, and cut-choose method, this paper designs a graphic similarity security decision protocol that can resist malicious adversary attacks. The analysis shows that the protocol has high computational efficiency and has wide application value in terrain matching, mechanical parts, biomolecules, face recognition, and other fields.
Open access
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf 1.
Open access
3 source records
cs.CV
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
In this paper, we explore Blockchain technology can be used to build a reliable decentralised authentication system. High security for the bioacoustics signal authentication mechanism is guaranteed by using an optimised number of secured features from the bioacoustics signal rather than conventional biometric features for authentication, and by utilising a blockchain model to improve the robustness of multiple checks on the data. It allows for trustworthy authentication and the tracking of terminal activity. Then, light weighted cryptography (LWC) is developed to offer protection at each edge node and terminal. Finally, the belief propagation (BP) algorithm for retraining the features of the bioacoustics signal serves as the foundation for the catching method. It improves hit ratio while decreasing delay time. The experimental setup uses the bioacoustics signals for authentication instead of conventional biometric features, and the use of a blockchain model for data transparency improves the efficiency of multiple checks. When this happens, privacy and safety are both boosted.
Unlike general passwords, user authentication technology using biometric data cannot be lost or forgotten, and it has the advantage of being impossible to forge or falsify by attackers. Since this biometric data contains sensitive information, a safe storage method is needed. However, if biometric data is managed on a central server, it is limited by being vulnerable to integrity infringement attacks by system attackers and persistent infringement attacks on the authentication service. To solve this problem, a model using blockchain-based information security technology is proposed in this paper. The aim is to safely manage the user biometric data by dispersing the storage of the biometric data feature information for user authentication in smart contracts and IPFS using Ethereum. We have verified that it takes an average of 1,472.733 ms and 217.829 ms, to register and authenticate a user in the proposed model and we expect that it will provide a reliable authentication service to the users compared to the existing authentication method in which the biometric data is managed by a single server.
Zengpeng Li, Mei Wang, Vishal Sharma, Prosanta Gope
Vehicle authentication is an essential component validating the vehicle’s identity and ensuring the integrity of transformed data for intelligent transport vehicles (ITS) in the vehicular ad hoc network (VANET). Easy to deploy and operate privacy-enhancing vehicle authentication mechanisms are the mainstay for the widespread ITS in the VANET. Very recently, VANET security architectures are constituting by IEEE 1609.2 group, NoW project, the SeVeCom project. However, these approaches heavily depend on the consuming public key infrastructure (PKI) and certification authorities (CA). In this work, walking along the research line, we attempt to design authentication protocols with two diverse factors for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks, respectively, without depending on the stumbling block PKI/CA. In addition, a smooth projective hash function (SPHF) (a.k.a., a special case of the designated-verifier zero-knowledge proof system) guarantees any recipient can confirm the authenticity and integrity of the received messages without knowing the authentication factors. Thus, to optimize the communication round, SPHF is used to design a (group) two-factor authenticated key exchange (AKE) with low-interactive communication rounds. The proof-of-concept implementation indicates that the computation and communication overheads introduced by our solution are acceptable in real-world deployments. The security of the proposed approach is validated using Bellare-Pointcheval-Rogaway (BPR) model along with the experimental evaluation and the theoretical analysis.
The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.
Abstract With the the advent era of big data, the secure computation calculates data on the premise of protecting data privacy, to realize the availability and invisibility of data. Secure multi-party computation, as one of three major technical tools of privacy computing, can still securely carry out data collaborative computation without a trusted third party. As an important branch of secure multi-party computation, the secure computing geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, which has important research significance. In this paper, the graphic similarity problem is studied. Firstly, this paper proposes the adjacency matrix vector coding method of isomorphic graphics and uses the Paillier variant cryptosystem to securely solve the graphic similarity judgment under the semi-honest model. By using an elliptic curve cryptosystem and zero-knowledge proof to solve the possible malicious attacks under the semi-honest model, a graphic similarity judgment protocol under the malicious model is designed. The protocol can resist malicious attacks, has high computational efficiency, and has wide application value.