The rapid growth of Electric Vehicles (EVs) has brought significant challenges in ensuring the privacy of sensitive data generated, particularly in Vehicle-to-Vehicle (V2V) energy trading systems. This study examines methods to balance data privacy preservation with the utility required for EV-related services. Existing privacy-preserving techniques often struggle to strike a balance between privacy and utility, particularly in dynamic environments where data sensitivity and usage patterns are constantly changing. In this paper, we propose an Adaptive Private Stable Matching (APSM) algorithm that incorporates a dynamic privacy budget algorithm for Differential Privacy (DP). APSM provides stable, privacy-preserving matches for EVs participating in V2V energy trading. The dynamic privacy budget mechanism adjusts allocation according to the number of EVs, offering enhanced privacy protection when necessary and increased utility when feasible. The proposed approach optimizes the utilization of the privacy budget, meeting both strict privacy requirements and ensuring efficient service delivery. Experimental results show that the technique outperforms static approaches in terms of privacy budget management, thereby enhancing privacy protection while maintaining high data utility. This combination renders APSM highly suitable for practical V2V energy trading scenarios, delivering robust privacy safeguards without compromising system performance.

Despite its success in many applications, federated learning is increasingly vulnerable to sophisticated poisoning attacks. Existing defenses, particularly Byzantine Robust Aggregation Rules (BRARs), offer some protection but rely on strong assumptions or challenging technical prerequisites. To address these shortcomings, we propose anensemble defense with provably convergent aggregation(EndPCA). By using the entropy weight method to consolidate scores from multiple BRARs into an ensemble trust score, it effectively integrates heterogeneous weak BRARs to resist a wide range of poisoning attacks under practical assumptions. We formally prove that EndPCA can provide theoretical guarantees of convergence with bounded error. Our empirical evaluations show that EndPCA consistently outperforms existing BRARs, demonstrating its effectiveness across various scenarios.

The desire to overcome reliability issues of the distributed energy resources (DERs) led researchers to develop a novel concept named virtual power plant (VPP). VPPs are supposed to carry out intelligent and secure energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain within a decentralized VPP network emerged as a novel paradigm. However, this decentralization also suffers with trust, reliability, energy management, and efficiency issues due to DERs dynamic nature. Thus, in this article, we first work to provide an efficient energy management strategy for VPPs to enhance demand response, then we propose an energy oriented trading and block mining protocol and name it as P roof o f E nergy M arket (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a P rivate PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as V irtual P rivate T rading (VPT). We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading-time profit values. Afterwards, we carry out simulation-based experiments of our proposed model to show the effectiveness and novelty as compared to state-of-the-art works.

The recent increase in Electric Vehicles (EVs) on the road has highlighted privacy concerns, particularly in the Vehicle-to-Vehicle (V2V) energy trading scenario. Ensuring location privacy in Vehicular Ad Hoc Networks (VANETs) is crucial for user confidentiality. Existing privacy techniques in the V2V paradigm protect the location coordinates of the EVs, but privacy risks persist after EVs are matched. In this paper, we introduce a dual-layer location privacy matching (DLLPM) technique to enhance the privacy of V2V matching. Our approach utilizes Laplace differential privacy and partial homomorphic encryption , ensuring that the EV’s private data remains inaccessible to both participants and adversaries. We introduce a noise addition and clipping algorithm to obfuscate EV coordinates within a defined radius. Encrypted distance-based preference lists are generated using partial homomorphic encryption to establish differentially private stable matches. DLLPM ensures EV location privacy throughout the matching process and mitigates the risk of location privacy leakage even after suppliers and demanders exchange location information . Theoretical analysis and experimental results confirm the efficiency of DLLPM, demonstrating robust privacy preservation with a computational complexity of O ( n 2 log n ⋅ ( C enc + C addHE + C subHE + C dec ) ) . We further evaluate computational performance using 128-bit and 256-bit encryption, showing that DLLPM achieves private and efficient matching in the V2V trading paradigm.

The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, traditional differentially private mechanisms tend to produce unbounded outputs in order to achieve maximum disturbance range, which is not always in line with real-world applications. Existing solutions attempt to address this issue by employing post-processing or truncation techniques to restrict the output results, but at the cost of introducing bias issues. In this paper, we propose a novel differentially private mechanism which uses a composite probability density function to generate bounded and unbiased outputs for any numerical input data. The composition consists of an activation function and a base function, providing users with the flexibility to define the functions according to the DP constraints. We also develop an optimization algorithm that enables the iterative search for the optimal hyper-parameter setting without the need for repeated experiments, which prevents additional privacy overhead. Furthermore, we evaluate the utility of the proposed mechanism by assessing the variance of the composite probability density function and introducing two alternative metrics that are simpler to compute than variance estimation. Our extensive evaluation on three benchmark datasets demonstrates consistent and significant improvement over the traditional Laplace and Gaussian mechanisms. The proposed bounded and unbiased composite differentially private mechanism will underpin the broader DP arsenal and foster future privacy-preserving studies.

In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay the minimum possible amount along with getting maximum benefits. In this context, usage based dynamic pricing strategies of DSM plays their role and provide users with specific incentives that help shaping their load curve according to the forecasted load. However, these reported real-time values can leak privacy of smart meter users, which can lead to serious consequences such as spying, etc. Moreover, most dynamic pricing algorithms charge all users equally irrespective of their contribution in causing peak factor. Therefore, in this paper, we propose a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate the concept of differential privacy to protect the privacy of real-time smart metering data. To calculate accurate billing, we also propose a noise adjustment method. Finally, we proposeDemandResponse enhancingDifferentialPricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. We also carry out theoretical analysis for differential privacy guarantees and for cooperative state probability to analyze behavior of cooperative smart meters. The performance evaluation of DRDP strategy at various privacy parameters show that the proposed strategy outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.11.A preliminary version has been published by 2020 IEEE International Conference on Communications (ICC 2020), June, 2020, Dublin, Ireland entitled Differentially Private Dynamic Pricing for Efficient Demand Response in Smart Grid.

Safety-critical vehicular applications require significant computation and communication resources and have strict performance requirements. Therefore, individual bandwidth reservation schemes are used to support such processes. Such schemes allow vehicles to place a cost-efficient smart reservation request alongside providing guaranteed bandwidth resources. However, efficient reservation is difficult to achieve due to uncertainty in both; future reservation requirement (i.e., demand) and network operator (NO) bandwidth cost. In order to solve this problem, a Heuristic Greedy Update Smart Reservation algorithm (HG-USR) is proposed by formulating bandwidth reservation cost problem. The primary objective of this formulation is to minimize the total reservation cost in certain problem scenarios, such as exact-booking, under-booking, and over-booking over time as the vehicle moves through the driving path. Extensive numerical studies have been carried out with the help of our proposed model. The experimental outcomes show that vehicle can successfully minimize the total cost of bandwidth resources as compared to prediction-based bandwidth reservation cost and immediate reservation request based approaches.

Efficient load utilization in order to match electric supply is one of the most considered factors in smart grid management. Demand side management (DSM) strategies such as real-time dynamic energy pricing has motivated customers to efficiently use their energy in order to reduce their bills intelligently. However, this real-time dynamic pricing can be a threat to privacy of smart homes inhabitants, as their lifestyle can easily be revealed via these real-time electricity values. Therefore, a strong privacy preserving strategy needs to be incorporated with real-time pricing. In this paper, we propose a Differentially private demand Response enhancing Dynamic Pricing (DRDP) strategy that incorporates the advantages of differential privacy, and usage based dynamic billing. The proposed strategy effectively protects user's privacy along with enhancing dynamic pricing by incentivizing the participating smart homes.

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more secure and private, we use differential privacy technique, which ensures that no adversary will be able to infer private information of any participant with confidence. Furthermore, to reduce computational complexity at every trading node, we use consortium blockchain, in which selected nodes are given authority to add a new block in the blockchain. Finally, we develop differentially private Energy Auction for bLockchain-based microgrid systems (DEAL). We compare DEAL with Vickrey-Clarke-Groves (VCG) auction scenario and experimental results demonstrates that DEAL outperforms VCG mechanism by maximizing sellers' revenue along with maintaining overall network benefit and social welfare.