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Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy. 

New breed of applications, such as autonomous driving and their need for computation-aided quick decision making has motivated the delegation of compute-intensive services (e.g., video analytic) to the more powerful surrogate machines at the network edge–edge computing (EC). Recently, the notion of pervasive edge computing (PEC) has emerged, in which users’ devices can join the pool of the computing resources that perform edge computing. Inclusion of users’ devices increases the computing capability at the edge (adding to the infrastructure servers), but in comparison to the conventional edge ecosystems, it also introduces new challenges, such as service orchestration (i.e., service placement, discovery, and migration). We propose uDiscover, a novel user-driven service discovery and utilization framework for the PEC ecosystem. In designing uDiscover, we considered the Named-Data Networking architecture for balancing users workloads and reducing user-perceived latency. We propose proactive and reactive service discovery approaches and assess their performance in PEC and infrastructure-only ecosystems. Our simulation results show that (i) the PEC ecosystem reduces the user-perceived delays by up to 70%, and (ii) uDiscover selects the most suitable server–"accurate" delay estimates with less than 10% error–to execute any given task. 

In this paper, we study efficient and authorized rewriting of transactions already written to a blockchain. Mutable transactions will make a fraction of all blockchain transactions, but will be a necessity to meet the needs of privacy regulations, such as the General Data Protection Regulation (GDPR). The state-of-the-art rewriting approaches have several shortcomings, such as being coarse-grained, inability to expunge data, absence of revocation mechanisms, lack of user anonymity, and inefficiency. We present ReTRACe, an efficient framework for transaction-level blockchain rewrites, that is fine-grained and supports revocation. ReTRACe is designed by composing a novel revocable chameleon hash with ephemeral trapdoor scheme, a novel revocable fast attribute based encryption scheme, and a dynamic group signature scheme. We discuss ReTRACe, and its constituent primitives in detail, along with their security analyses, and present experimental results to demonstrate scalability. 

We propose a novel framework for off-chain execution and verification of computationally-intensive smart contracts. Our framework is the first solution that avoids duplication of computing effort across multiple contractors, does not require trusted execution environments, supports computations that do not have deterministic results, and supports general-purpose computations written in a high-level language. Our experiments reveal that some intensive applications may require as much as 141 million gas, approximately 71x more than the current block gas limit for computation in Ethereum today, and can be avoided by utilizing the proposed framework. 

The current centralized model of the electricity market is not efficient in performing distributed energy transactions required for the transactive smart grid. One of the prominent solutions to this issue is to integrate blockchain technologies, which promise transparent, tamper-proof, and secure transaction systems specifically suitable for the decentralized and distributed energy markets. Blockchain has already been shown to successfully operate in a microgrid peer-to-peer (P2P) energy market. The prime determinant of different blockchain implementations is the consensus algorithm they use to reach consensus on which blocks/transactions to accept as valid in a distributed environment. Although different blockchain implementations have been proposed independently for P2P energy market in the microgrid, quantitative experimental analyses and comparison of the consensus algorithms that the different blockchains may use for energy markets, has not been studied. Identifying the right consensus algorithm to use is essential for scalability and operation of the energy market. To this end, we evaluate three popular consensus algorithms: (i) proof of work (PoW), (ii) proof of authority (PoA), and (iii) Istanbul Byzantine fault tolerance (IBFT), running them on a network of nodes set up using a network of docker nodes to form a microgrid energy market. Using a series of double auctions, we assess each algorithm's viability using different metrics, such as time to reach consensus and scalability. The results indicate that PoA is the most efficient and scalable consensus algorithm to hold double auctions in the smart grid. We also identified the minimum hardware specification necessary for devices such as smart meters, which may run these consensus algorithms