publications

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ’15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

The adoption of blockchain for Transactive Energy has gained significant momentum as it allows mutually non-trusting agents to trade energy services in a trustless energy market. Research to date has assumed that the built-in Byzantine Fault Tolerance in recording transactions in a ledger is sufficient to ensure integrity. Such work must be extended to address security gaps including random bilateral transactions that do not guarantee reliable and efficient market operation, and market participants having incentives to cheat when reporting actual production/consumption figures. Work herein introduces the Electron Volt Exchange framework with the following characteristics: (1) a distributed protocol for pricing and scheduling prosumers’ production/consumption while keeping constraints and bids private, and (2) a distributed algorithm to prevent theft that verifies prosumers’ compliance to scheduled transactions using information from grid sensors (such as smart meters) and mitigates the impact of false data injection attacks. Flexibility and robustness of the approach are demonstrated through simulation and implementation using Hyperledger Fabric.

In this paper, we consider the problem of solving a distributed (consensus-based) optimization problem in a network that contains regular and malicious nodes (agents). The regular nodes are performing a distributed iterative algorithm to solve their associated optimization problem, while the malicious nodes inject false data with a goal to steer the iterates to a point that serves their own interest. The problem consists of detecting and isolating the malicious agents, thus allowing the regular nodes to solve their optimization problem. We propose a method to dwarf data injection attacks on distributed optimization algorithms, which is based on the idea that the malicious nodes (individually or in collaboration) tend to give themselves away when broadcasting messages with the intention to drive the consensus value away from the optimal point for the regular nodes in the network. In particular, we provide a new gradient-based metric to detect the neighbors that are likely to be malicious. We also provide some simulation results demonstrating the performance of the proposed approach.

Decentralized optimization has found a significant utility in recent years, as a promising technique to overcome the curse of dimensionality when dealing with large-scale inference and decision problems in big data. While these algorithms are resilient to node and link failures, they however, are not inherently Byzantine fault-tolerant towards insider data injection attacks. This paper proposes a decentralized robust subgradient push (RSGP) algorithm for detection and isolation of malicious nodes in the network for optimization non-strongly convex objectives. In the attack considered in this work, the malicious nodes follow the algorithmic protocols, but can alter their local functions arbitrarily. However, we show that in sufficiently structured problems, the method proposed is effective in revealing their presence. The algorithm isolates detected nodes from the regular nodes, thereby mitigating the ill-effects of malicious nodes. We also provide performance measures for the proposed method.

In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians’ actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions.

Power dissipation has become the major concern for circuit design and implementation. Reversible Logic is the best alternative to Irreversible Logic in terms of low power consumption. Circuits designed using reversible logic have a wide array of applications. The Quantum Cost, Garbage Outputs, Ancillary Inputs and Delay are some of the parameters of reversible circuits that can be used to determine their efficiency and compare them with existing works. Optimization of these parameters are highly essential. Garbage Outputs is an important parameter that must be considered. This paper presents a design for a Reversible Radix-4 Booth Multiplier that is optimized in Garbage Cost and Ancillary inputs. The design proposed is capable of both signed and unsigned multiplication. The optimization in Garbage Cost ensures lower heat dissipation. The Encoded Booth Algorithm or Radix-4 Booth Algorithm reduces the number of partial products generated in signed multiplication to half the number generated using a Radix-2 signed multiplier making it suitable for Digital Signal Processors. The design proposed is compared to existing multiplier circuits and the parameters are tabulated.