Untrusted Environment Research Articles

A Three-Party Game Based on Trust for IoT Task Delegation in an Untrusted Environment

With the rapid development of technologies and applications, extending from trusted environments to untrusted environments is the development trend of Internet of Things (IoT) applications. Due to the limited resources of nodes and the rise of edge computing, more and more IoT applications are required to have task delegation functions. Delegating IoT tasks in untrusted environments has to solve two problems: First, how to evaluate the trustworthiness of nodes so that requesters can delegate tasks to trusted providers; the second is how to ensure the successful implementation of the task delegation between the requester and the provider. While trust models are often used to solve the first problem, the lack of ability to describe transactions between the requester and the provider makes them unsuitable for solving the second problem. In recent years, game theory has been more applied to task delegation. By setting appropriate utility functions, game theory can ensure the successful implementation of task delegation. However, since it is unable to evaluate the trustworthiness of nodes, game theory cannot solve the first problem. This paper combines the advantages of trust models and game theory to construct a game of three-party task delegation based on trust to explore a solution to problems of IoT task delegation in an untrusted environment. Finally, aiming at the problem that setting essential parameter values of trust models is empirical and experimental, this paper theoretically discusses how to set these values in an untrusted environment.

DPTracer: Integrating Log-Driven Accountability into Data Provision Networks

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision networks (DPNs) and prioritize accuracy and reliability over delivery efficiency. However, the effectiveness of the security mechanisms of DPNs, such as self-correction, is limited without a fine-grained log of node activities. This paper presents DPTracer: a novel logging system designed for DPNs that uses tamper-evident logging to address the challenges of maintaining a reliable log in an untrusted environment of DPNs. By integrating logging and validation into the data provisioning process, DPTracer ensures comprehensive logs and continuous auditing. Our system uses Process Tree as a data structure to store log records and generate proofs. This structure permits validating node activities and reconstructing historical data provision processes, which are crucial for self-correction and verifying data sufficiency before results are finalized. We evaluate the overheads introduced by DPTracer regarding computation, memory, storage, and communication. The results demonstrate that DPTracer incurs reasonable overheads, making it practical for real-world applications. Despite these overheads, DPTracer enhances security by protecting DPNs from post-process and in-process tampering.

Entropic uncertainty principle for mixed states

The entropic uncertainty principle in the form proven by Maassen and Uffink yields a fundamental inequality that is prominently used in many places all over the field of quantum information theory. In this paper, we provide a family of versatile generalizations of this relation. Our proof methods build on a deep connection between entropic uncertainties and interpolation inequalities for the doubly stochastic map that links probability distributions in two measurement bases. In contrast to the original relation, our generalization also incorporates the von Neumann entropy of the underlying quantum state. These results can be directly used to bound the extractable randomness of a source-independent quantum random number generator in the presence of fully quantum attacks, to certify entanglement between trusted parties, or to bound the entanglement of a system with an untrusted environment. Published by the American Physical Society 2024

TrustHealth: Enhancing eHealth Security with Blockchain and Trusted Execution Environments

The rapid growth of electronic health (eHealth) systems has led to serious security and privacy challenges, highlighting the critical importance of protecting sensitive healthcare data. Although researchers have employed blockchain to tackle data management and sharing within eHealth systems, substantial privacy concerns persist as a primary challenge. In this paper, we introduce TrustHealth, a secure data sharing system that leverages trusted execution environment (TEE) and blockchain technology. TrustHealth leverages blockchain to design smart contracts to offer robust hashing protection for patients’ healthcare data. We provide a secure execution environment for SQLCipher, isolating all sensitive operations of healthcare data from the untrusted environment to ensure the confidentiality and integrity of the data. Additionally, we design a TEE-empowered session key generation protocol that enables secure authentication and key sharing for both parties involved in data sharing. Finally, we implement TrustHealth using Hyperledger Fabric and ARM TrustZone. Through security and performance evaluation, TrustHealth is shown to securely process massive encrypted data flows at a rate of 5000 records per second, affirming the feasibility of our proposed scheme. We believe that TrustHealth offers valuable guidelines for the design and implementation of similar systems, providing a valuable contribution to ensuring the privacy and security of eHealth systems.

A contrastive learning approach for enhanced robustness for strengthening federated intelligence in internet of visual things

As the Internet of Things (IoT) continues to grow, edge intelligence has developed as a favorable paradigm to enable effective and instantaneous processing of data at the network's edge. The deployment of an edge intelligence approach in untrusted environments exposes them to adversarial attacks, posing a substantial challenge to the reliability and confidentiality of the learned models especially in federated settings. Among these attacks, the white-box attacks are the most dangerous and challenging to defend in IoT systems. This study proposes a novel approach to robustify the federated edge intelligence technique by integrating the power of contrastive learning to enhance the model's ability to distinguish between clean and adversarial examples, thereby improving its robustness. Our robustification approach is based on two lines of defense. First, an elegant federated contrastive pretraining is introduced to robustify the model by adversarially training it using a label-free representation attack. Second, we introduce a novel Consistency Regularized Triplet (CRT) loss function that encourages the regularization of the federated edge intelligence model to learn discriminative representations while minimizing the influence of adversarial perturbations. The empirical evaluation of the Food-101 and CIFAR-100 datasets revealed that the proposed method outperformed the state-of-the-art robust methods on both standard accuracy and robustness accuracy.

A general framework for intelligent IoT data acquisition and sharing in an untrusted environment based on blockchain

A general framework for intelligent IoT data acquisition and sharing in an untrusted environment based on blockchain

A Blockchain-based federated learning framework for secure aggregation and fair incentives

Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.

Evaluation Performance of Bloom Filter in Blockchain Network

A blockchain is a secret, scalability and decentralized p2p network in which all nodes follow similar protocols, preventing any single node from controlling the basic structure. In blockchain, Bloom filters are used to preserve the privacy of lightweight nodes. Bloom filter is a memory efficient randomized data structure to represent a set in order to support associate queries. Bloom filters offer a trade-off between (elements size in the network and bandwidth) and privacy metrics in untrusted environments. This paper proposes an analysis to evaluate the performance of Bloom filters. The evaluation results are based on the statistical distribution, standard deviation, entropy and y-deniability that are used by the attacker to analysis the leakage of the Bloom filter algorithm. Experimental results show that the degree of privacy in the network needs a large amount of elements and more bandwidth using the Bloom filter algorithm.

A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

A Safe and Reliable Approach to Transfer Health Records using Searchable Key Proxy Re-Encryption

In the delivery of high-quality healthcare services, the exchange of computerized clinical data holds significant importance. To safeguard the confidentiality of client's sensitive information, it is imperative to adhere to stringent privacy and security regulations when sharing this data with others. Consequently, the adoption of proxy re-encryption servers serves as an effective and reliable means of transmitting computerized medical information. This encryption method is currently recognized as the sole approach that is both unidirectional and recursive, allowing third parties to modify encrypted ciphertext intended for one party so that it can be deciphered by another. This technique is referred to as proxy re-encryption. In order to empower patients with control over who can access their healthcare data, the system operates as an intermediary between them and a database server. Prior to transferring files to the information server, individuals who own them can utilize their keys to encrypt the files, thus ensuring the security of the data stored there. The test results demonstrate the system's proficiency in supporting secure data sharing. These proxy re-encryption mechanisms guarantee the confidentiality and accessibility of data, offering end-to-end security and privacy for electronically stored health information. While prioritizing patient privacy, the system aims to enhance collaboration among providers and facilitates data sharing between the sender and receiver using their respective keys. In this context, a unidirectional system operates in a one-way, providing a higher level of security, making it a suitable choice for untrusted environments where message transport is necessary without granting the receiver rights to respond to it.

Making tradable white certificates trustworthy, anonymous, and efficient using blockchains

Fossil fuel pollution has contributed to dramatic changes in the Earth’s climate, and this trend will continue as fossil fuels are burned at an ever-increasing rate. Many countries around the world are currently making efforts to reduce greenhouse gas emissions, and one of the methods is the Tradable White Certificate (TWC) mechanism. The mechanism allows organizations to reduce their energy consumption to generate energy savings certificates, and those that achieve greater energy savings can sell their certificates to those that fall short. However, there are some challenges to implementing this mechanism, such as the centralized and costly verification and control of energy savings. Moreover, the verification process is not transparent, which could lead to fraud or manipulation of the system. Therefore, in this paper, we propose a blockchain-based TWC mechanism to automatically create, verify, and audit the TWC certificates. In addition, we propose a smart-contract-based TWC trading mechanism that enables traders to trade their TWCs without exposing their private information in an untrusted environment. Evaluations show that the proposed TWC framework is scalable for 1000 TWC traders simultaneously, and optimization problem can be solved in less than 120ms. Moreover, it has been shown that Polygon Matic incurs least gas cost compared to other blockchain-based solutions.

Smaug: A TEE-Assisted Secured SQLite for Embedded Systems

As one of the most popular relational databases for embedded devices, SQLite is lightweight to be embedded into applications without installing a specific database management system. However, simplicity and easy-to-use are double-edged swords; while bringing convenience, they also make data processing and storage risky. For example, an attacker can obtain data from a database file or memory and tamper with it once he has gained higher privileges, threatening the database's confidentiality and integrity. To address such security issues, based on a trusted execution environment (TEE) and a trusted platform module (TPM), we have proposed Smaug, a general secure scheme to ensure the confidentiality and integrity of SQLite and similar databases. With Smaug, all the critical data is stored in ciphertext, and data integrity protection is also provided. Besides, with TEE, all the sensitive operations are isolated from the untrusted environment, which can effectively resist attacks against memory. In addition, we use TPM to provide a solid root-of-trust (RoT) for the system. Finally, we have implemented a prototype system, and the performance evaluations have clarified the dominant factors that affect the system availability, providing a reference to the design and implementation of similar systems.

Automatic Modulation Classification via Meta-Learning

Internet of Things (IoT) networks are often subject to many malicious attacks in untrusted environments, and Automatic Modulation Classification (AMC) is an effective way to combat IoT physical-layer threats. However, most existing AMC methods assume sufficient labeled signals and invariant signal distribution, which is often impossible in untrusted environments. In this paper, a new meta-learning method is proposed for few-shot AMC with distribution bias. First, a Multi-Frequency Octave ResNet (MFOR) is constructed to learn coarse (low-frequency) and fine (high-frequency) features, which can efficiently identify the modulation type of the signal while saving computational resources. Second, a large number of classification-related meta-tasks are established for training MFOR to explore general knowledge in signal classification, and then transfer it to the AMC. Different with DNNs that learn a mapping by multiple instances, the MFOR with Meta-learning (denoted as M-MFOR) can improve the generalization ability of new AMC tasks with very few instances and distribution bias. Furthermore, we find that the distribution bias between data can be reduced by adjusting the normalized distribution and propose a class-related mixup. Extensive experiments are taken on several datasets to investigate the effectiveness of M-MFOR. The results show its feasibility and superiority over existing methods.

Performance Analysis of Scattered Record Utilizing Block chain for Identifying Visual Substance Imitation

Visual content captured by monitoring devices are vulnerable to local or remote malicious modifications of visual information by offenders. These malicious visual content code changes (called forgeries) are divided into Cloning, Splicing, Inter-frame and Intra-frame forgeries. Rapidly improving editing software tools have made visual content manipulation feasible. Consequently, malicious attackers are trying to manipulate the visual content. Detecting visual content tampering is a major need for many applications Distributed databases can be used to detect visual content tampering by storing the evidence created by hashing the visual content at the source itself. However, these distributed databases lack in data transparency and security against Byzantine failures in an untrusted environment. Blockchain is a technology with the inherent ability to store data in a chronological chained link of events: establishing an irrefutable database. Using cryptographic hashes together with blockchain one can generate cryptographic hashes of the data content from a video recording, and consequently transmit these hashes to a blockchain. In this thesis we have proposed a model called Evidence chain based on Blockchain to ensure the credibility of the visual content. Unlike bitcoin which is a digital currency the proposed system documents content hash by using IPFS, Hash based technology, RSA, Elliptic Curve Cryptography. Image and Video segments are hashed and stored in chronological order as a chain of blocks which are detectable and non-altering guaranteeing the validity of the information. This research is significant in establishing the trust between any two parties. Key words: Block chain, Intra-frame, IPFS, Elliptic Curve Cryptography.

Redactable Blockchain-Enabled Hierarchical Access Control Framework for Data Sharing in Electronic Medical Records

The application of blockchain to data sharing in an untrusted environment has received widespread industry attention in recent years. However, the tamper-proof property of blockchain brings protection for sharing data and also leads to a new limitation for the deletion of malicious data. Nowadays, many methods based on redactable blockchain with a chameleon hash is proposed to attempt to solve the above problem, but loss of effective access control to the modifiers can easily lead to breaking the positive characteristics of the blockchain. To address the aforementioned problem, a hierarchical access control redactable blockchain model for data sharing through attribute-based encryption and a chameleon hash is introduced. Under this model, the data owner can specify who can modify their data by setting an access policy and authenticating the modifier with a digital signature. The data owner's full control over rights is guaranteed, while the modifier's behavior is regulated. In addition, the hierarchical access control decentralized technique solves the overpowerful problem of center authority and single-point-of-failure problems in the existing works. The security analysis indicates that the proposed scheme achieves indistinguishable chosen plaintext attack security as well as collision resistance security. Performance analysis shows that the proposed scheme is more complete and has better efficiency compared with other schemes.

Efficient Asynchronous Federated Learning Research in the Internet of Vehicles

Federated Learning (FL) is a distributed machine learning paradigm that ensures data do not leave local devices. Data sharing problems can be addressed by FL in untrusted environments, e.g., the Internet of Vehicles (IoV). However, FL needs to frequently exchange massive parameters to achieve preset model goals. In addition, the change in bandwidths and the delay of data communications due to user mobility challenge the synchronization of model parameters. In this paper, an Efficient Hierarchical Asynchronous Federated Learning (EHAFL) algorithm is proposed to adjust the encoding length dynamically according to the bandwidth and reduce the communication cost substantially. A dynamic hierarchical asynchronous aggregation mechanism is proposed leveraging gradient sparsification and asynchronous aggregation techniques to further reduce the communication costs and improve the aggregation efficiency of the global model. Simulation results on MNIST and real-world datasets show that our proposed solution can reduce the communication costs by 98% while only compromising the model accuracy by 1%.

SEAL: A Strategy-Proof and Privacy-Preserving UAV Computation Offloading Framework

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

Detection and Prevention of Worm hole Attack to Improve Traffic in VANET

Abstract: A Wireless Sensor Network (WSN) made up of spatially dispersed independent devices using sensors is vulnerable to many different types of threats and attacks due to a number of factors, including the unattended deployment in an untrusted environment, the limited network resources, the ease of network access, and the range of radio transmission. One of these weaknesses is the wormhole attack, in which a hacker sets up a low-latency link between sensor nodes in order to sabotage them, use up network resources, and steal sensitive information. This essay outlines the WSN wormhole attack and provides a critical assessment of the defences. Wireless sensor network defences against wormhole attacks and wormhole detection methods are contrasted and evaluated for their efficacy (WSN). Because there are unwanted attackers present, secure communication is absolutely necessary for the community. The bundle appears to be disintegrating, but most likely due to the attacker, according to the wormhole. If the intrusion is discovered in time, the perpetrator can be found and expelled from the network before any potentially dangerous actions are taken or data is corrupted. Similar to that, the suggested Worm hole serves as a deterrent and protects you. Information on entrance tactics that might be utilised to protect the access point can be found using the suggested device. The performance of the prior device, which is also the best and safest network to use, can be used to assess the correctness of the results. The packets connecting the nodes to the network can be used to calculate the agreement's cost

ABC-GSPBFT: PBFT with grouping score mechanism and optimized consensus process for flight operation data-sharing

The flight operation data sharing not only brings huge benefits to all participants, but also puts forward higher requirements for data security. Consortium blockchain provides a new approach to safely sharing flight operation data in an untrusted environment. Practical Byzantine Fault-Tolerant (PBFT) algorithm is one of the most widely used consensus algorithms in consortium blockchain, but it exists some problems, such as static structure, low consensus efficiency and large communication overhead. To solve these problems, a PBFT with grouping score mechanism and artificial bee colony-optimized consensus process, namely ABC-GSPBFT, is designed to construct the consortium blockchain for sharing flight operation data. Firstly, the artificial bee colony algorithm is used to pre-select a collection of reliable nodes to narrow the scope of consensus. Then, the grouping score mechanism is employed to dynamically update the consensus nodes to further narrow the scope of consensus and enhance the reliability of consensus nodes. Finally, the commit stage of the PBFT algorithm is reasonably simplified to improve consensus efficiency. The actual flight operation data is selected to test and verify the effectiveness of the ABC-GSPBFT algorithm. The analysis and experimental results show that the ABC-GSPBFT algorithm can greatly reduce consensus delay and communication overhead, and improve dynamic performance and consensus efficiency of the flight data sharing. It is an effective method for safely, reliably and efficiently sharing flight data.

Intelligent Information Network Security Management Strategy for Service Innovation of Manufacturing Enterprises Under Blockchain

Since the beginning of the new century, all manufacturing enterprises have been seeking ways to transform and upgrade the systems to maintain their competitive advantages. In the process of transformation and upgrading, service innovation has become more vital. In the service innovation mode, customers and suppliers are mainly involved in the manufacture of products. In the process of communication, these suppliers and customers need to rely on public key infrastructure (PKI) for data encryption and identity authentication operations, thereby achieving reliable information exchange and identity authentication in an untrusted environment. Therefore, ensuring the security of network communication for various types of users is crucial. The purpose of this paper is to solve the problem that results when a tampered certificate is difficult to trace due to the single point of failure and vulnerability of the current settlement and authentication system. In this scenario, the user's identity information is leaked in the certificate application. Resolving this problem is necessary to ensure the security of the information network.