Model pruning has gained traction as a promising defense strategy against backdoor attacks in deep learning. However, existing pruning-based approaches often fall short in accurately identifying and removing the specific parameters responsible for inducing backdoor behaviors. Despite the dominance of fine-tuning-based defenses in recent literature, largely due to their superior performance, pruning remains a compelling alternative, offering greater interpretability and improved robustness in low-data regimes. In this paper, we propose a novel pruning approach featuring a learned \emph{selection} mechanism to identify parameters critical to both main and backdoor tasks, along with an \emph{invertible} pruning mask designed to simultaneously achieve two complementary goals: eliminating the backdoor task while preserving it through the inverse mask. We formulate this as a bi-level optimization problem that jointly learns selection variables, a sparse invertible mask, and sample-specific backdoor perturbations derived from clean data. The inner problem synthesizes candidate triggers using the inverse mask, while the outer problem refines the mask to suppress backdoor behavior without impairing clean-task accuracy. Extensive experiments demonstrate that our approach outperforms existing pruning-based backdoor mitigation approaches, maintains strong performance under limited data conditions, and achieves competitive results compared to state-of-the-art fine-tuning approaches. Notably, the proposed approach is particularly effective in restoring correct predictions for compromised samples after successful backdoor mitigation.

In this paper, given a user's query set and budget, we aim to use the limited budget to help users assemble a set of datasets that can enrich a base dataset by introducing the maximum number of distinct tuples (i.e., maximizing distinctiveness). We prove this problem to be NP-hard. A greedy algorithm using exact distinctiveness computation attains an approximation ratio of (1-e-1 )/2, but it lacks efficiency and scalability due to its frequent computation of the exact distinctiveness marginal gain of any candidate dataset for selection. This requires scanning through every tuple in candidate datasets and thus is unaffordable in practice. To overcome this limitation, we propose an efficient machine learning (ML)-based method for estimating the distinctiveness marginal gain of any candidate dataset. This effectively eliminates the need to test each tuple individually. Estimating the distinctiveness marginal gain of a dataset involves estimating the number of distinct tuples in the tuple sets returned by each query in a query set across multiple datasets. This can be viewed as the cardinality estimation for a query set on a set of datasets, and the proposed method is the first to tackle this cardinality estimation problem. This is a significant advancement over prior methods that were limited to single-query cardinality estimation on a single dataset and struggled with identifying overlaps among tuple sets returned by each query in a query set across multiple datasets. Extensive experiments using five real-world data pools demonstrate that our algorithm, which utilizes ML-based distinctiveness estimation, outperforms all relevant baselines in effectiveness, efficiency, and scalability. A case study on two downstream ML tasks also highlights its potential to find datasets with more useful tuples to enhance the performance of ML tasks.

Integrating Distributed Energy Resources (DERs) into smart grids presents challenges in privacy and transparency for Demand Response (DR) programs. Blockchain offers a secure, but transparent solution, risking privacy. ‘CypherChain’ is introduced as a novel framework for these programs, utilizing Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and Hypergraph Coloring via CHAIN and CYPHER protocols. These ensure private data aggregation and encrypted processing, balancing privacy with transparency. Tested on real-world smart building data, CypherChain improved data aggregation speed by 40% and cut computational costs by 30% against existing systems, showcasing its potential to revolutionize privacy in smart grids and address DR programs’ privacy-transparency issues.

The emergence of the Internet of Things (IoT), Industry 5.0 applications and associated services have caused a powerful transition in the cyber threat landscape. As a result, organisations require new ways to proactively manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing. However, in many cases, CTI contains sensitive information that has the potential to leak valuable information or cause reputational damage to the sharing organisation. While a number of existing CTI sharing approaches have utilised blockchain to facilitate privacy, it can be highlighted that a comprehensive approach that enables dynamic trust-based decision-making, facilitates decentralised trust evaluation and provides CTI producers with highly granular sharing of CTI is lacking. Subsequently, in this paper, we propose a blockchain-based CTI sharing framework, called Priv-Share, as a promising solution towards this challenge. In particular, we highlight that the integration of differential sharing, trustless delegation, democratic group managers and incentives as part of Priv-Share ensures that it can satisfy these criteria. The results of an analytical evaluation of the proposed framework using both queuing and game theory demonstrate its ability to provide scalable CTI sharing in a trustless manner. Moreover, a quantitative evaluation of an Ethereum proof-of-concept prototype demonstrates that applying the proposed framework within real-world contexts is feasible.

In this paper, we study how to acquire labeled data points from a large data pool to enrich a training set for enhancing supervised machine learning (ML) performance. The state-of-the-art solution is the clustering-based training set selection (CTS) algorithm, which initially clusters the data points in a data pool and subsequently selects new data points from clusters. The efficiency of CTS is constrained by its frequent retraining of the target ML model, and the effectiveness is limited by the selection criteria, which represent the state of data points within each cluster and impose a restriction of selecting only one cluster in each iteration. To overcome these limitations, we propose a new algorithm, called CTS with incremental estimation of adaptive score (IAS). IAS employs online learning, enabling incremental model updates by using new data, and eliminating the need to fully retrain the target model, and hence improves the efficiency. To enhance the effectiveness of IAS, we introduce adaptive score estimation, which serves as novel selection criteria to identify clusters and select new data points by balancing trade-offs between exploitation and exploration during data acquisition. To further enhance the effectiveness of IAS, we introduce a new adaptive mini-batch selection method that, in each iteration, selects data points from multiple clusters rather than a single cluster, hence eliminating the potential bias due to using only one cluster. By integrating this method into the IAS algorithm, we propose a novel algorithm termed IAS with adaptive mini-batch selection (IAS-AMS). Experimental results highlight the superior effectiveness of IAS-AMS, with IAS also outperforming other competing algorithms. In terms of efficiency, IAS takes the lead, while the efficiency of IAS-AMS is on par with that of the existing CTS algorithm.

6G-enabled IoT demands effectively utilising scarce resources to provide massive scale in network capacity. While blockchain-based resource sharing schemes have been proposed to enable effective resource allocation, they alone cannot ascertain the trust in the participating nodes, as they do not monitor node activities during the resource sharing. Trust and Reputation Management (TRM) can potentially solve these trust issues. However, changeable keys employed in blockchains to improve privacy preservation may render the TRM unusable, as the same node is no longer identifiable by a single key to which the trust and reputation scores are bound. This paper proposes a privacy-preserving TRM for blockchain-based resource sharing in 6G-enabled IoT networks. Our solution employs interconnected public-private blockchains, namely Isolated Identity Chain and Main Resource-sharing Chain to protect nodes' identity. Our TRM framework allows the nodes to use changeable keys in each transaction, making it impossible to trace the sharing history. The experimental results on a proof-of-concept implementation indicate the feasibility of our framework as it only incurs minimal overheads.

Blockchain technology can provide immutability, provenance and traceability in supply chains. To utilize Blockchain's full potential, it is important to link supply chain events to the relevant entities for traceability and accountability purposes. Authorized participation is realised through consortium of various organisations. Transactions are verified by peer nodes pertaining to the consortium. Hence, privacy preservation of trade sensitive information such as trade flows and locations of production, storage and retail sites cannot be ascertained. In this work, we propose a privacy-preservation framework, TradeChain, which decouples the trade events of participants using decentralised identities. TradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the following novel contributions: a) it incorporates two separate ledgers: a public permissioned blockchain for maintaining identities and the permissioned blockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs) on traders' private credentials to prove multiple identities on trade ledger and c) allows data owners to define dynamic access rules for verifying traceability information from the trade ledger using access tokens and Ciphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept implementation of TradeChain is presented on Hyperledger Indy and Fabric and an extensive evaluation of execution time, latency and throughput reveals minimal overheads.

Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as user’s attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts.

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.

Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable blockchain can not be altered or deleted. However, blockchain mechanisms do not guarantee the trustworthiness of data at the origin. We propose a layered architecture for improving the end-to-end trust that can be applied to a diverse range of blockchain-based IoT applications. Our architecture evaluates the trustworthiness of sensor observations at the data layer and adapts block verification at the blockchain layer through the proposed data trust and gateway reputation modules. We present the performance evaluation of the data trust module using a simulated indoor target localization and the gateway reputation module using an end-to-end blockchain implementation, together with a qualitative security analysis for the architecture.

We propose new rate-flexible low-density parity-check (LDPC) coding schemes for secrecy over a compound channel with L parallel links. These codes, called anti-root LDPC codes, have good performance at both finite and asymptotic code length while all links are jointly encoded. Firstly, an algebraic security scheme is developed based on the anti-root LDPC ensemble and a source splitter. Secondly, an information theoretic security scheme is built from the same splitter with the adjunction of a random sequence. Then, we present a new diversity-security tradeoff for channels exhibiting block fading or block erasure. Finally, we describe anti-root LDPC ensembles with higher diversity or security orders to attain the aforementioned tradeoff.

Energy minimization in wireless sensor networks is a key issue, particularly in order to maximize the lifetime of nonserviceable battery powered devices in the field. In this paper, we propose a new energy consumption model that allows allocation of variable transmit power and data compression/transmission rate to each sensor node. In contrast to previous work, our model captures both the cost of wireless transmission as well as the cost for a node to be in an active mode during transmission. This is an important and non-trivial consideration, as higher data rates require greater transmit power for reliable wireless transmission, however this reduces the duration for which the node must be in active mode. Based on this new model, we consider minimization of the total energy cost of lossless data gathering by using joint power and rate allocation under rate and capacity constraints. We show how the rate and power allocation problems are coupled in general, and we provide a convex optimization formulation for the resulting joint optimization problem. We present a special case for which power allocation separates from the rate allocation problem, namely when the cost of being active dominates. For this special case we obtain a simple characterization of the optimum power and rate allocation strategy. Simulation results are given in line with the theoretical analysis.

We consider peer-to-peer video broadcasting using cooperation among peers in an ad hoc wireless network. As opposed to the traditional single hop broadcasting, multiple hops cause an increase in broadcast video quality while creating interference and increasing transmission delay. We develop heuristics for the NP-complete problem of finding the subset of cooperating peers and the number of hops to maximize the data rate and to minimize the maximum observed delay in the system. Simulations show that using the proposed heuristics, different video sequences can be viewed at high qualities with acceptable delay among all peers.