Optimization of privacy-utility trade-offs under informational self-determination

The pervasiveness of Internet of Things results in vast volumes of personal data generated by smart devices of users (data producers) such as smart phones, wearables and other embedded sensors. It is a common requirement, especially for Big Data analytics systems, to transfer these large in scale and distributed data to centralized computational systems for analysis. Nevertheless, third parties that run and manage these systems (data consumers) do not always guarantee users’ privacy. Their primary interest is to improve utility that is usually a metric related to the performance, costs and the quality of service. There are several techniques that mask user-generated data to ensure privacy, e.g. differential privacy. Setting up a process for masking data, referred to in this paper as a ‘privacy setting’, decreases on the one hand the utility of data analytics, while, on the other hand, increases privacy. This paper studies parameterizations of privacy settings that regulate the trade-off between maximum utility, minimum privacy and minimum utility, maximum privacy, where utility refers to the accuracy in the estimations of aggregation functions. Privacy settings can be universally applied as system-wide parameterizations and policies (homogeneous data sharing). Nonetheless they can also be applied autonomously by each user or decided under the influence of (monetary) incentives (heterogeneous data sharing). This latter diversity in data sharing by informational self-determination plays a key role on the privacy-utility trajectories as shown in this paper both theoretically and empirically. A generic and novel computational framework is introduced for measuring privacy-utility trade-offs and their Pareto optimization. The framework computes a broad spectrum of such trade-offs that form privacy-utility trajectories under homogeneous and heterogeneous data sharing. The practical use of the framework is experimentally evaluated using real-world data from a Smart Grid pilot project in which energy consumers protect their privacy by regulating the quality of the shared power demand data, while utility companies make accurate estimations of the aggregate load in the network to manage the power grid. Over 20,000 differential privacy settings are applied to shape the computational trajectories that in turn provide a vast potential for data consumers and producers to participate in viable participatory data sharing systems

Data Privacy in the Smart Grid: A Decentralized Approach

Evolution toward the smart grid includes implementation of elements such as smart meters, embedded microprocessors, two-way communication systems from consumers to system operators, and automated demand response as supported through dynamic pricing. Dynamic pricing throughout the smart grid will require frequent transfer of energy consumption data from the customers to the ISOs. Privacy and security issues related to transferring this data are widely studied. However, typical frameworks rely on a trusted third party, such as the ISO or a load aggregator, that would then have access to all of the consumer data. This paper proposes a Bitcoin-like decentralized model as a solution for secure information transfer within the smart grid, eliminating the presence of a centralized data aggregator or other third party operator. Each smart meter participates as an equal peer in the proposed peer-to-peer network, and elements of authentication, confidentiality and data verification are developed similar to the existing Bitcoin framework. The contribution of this paper is the proposed framework for the smart grid which cryptographically secures the transfer of energy consumption data while ensuring privacy

On Privacy Preservation of Electric Vehicle Charging Control via State Obfuscation

The electric vehicle (EV) industry is rapidly evolving owing to advancements in smart grid technologies and charging control strategies. While EVs are promising in decarbonizing the transportation system and providing grid services, their widespread adoption has led to notable and erratic load injections that can disrupt the normal operation of power grid. Additionally, the unprotected collection and utilization of personal information during the EV charging process cause prevalent privacy issues. To address the scalability and data confidentiality in large-scale EV charging control, we propose a novel decentralized privacy-preserving EV charging control algorithm via state obfuscation that 1) is scalable w.r.t. the number of EVs and ensures optimal EV charging solutions; 2) achieves privacy preservation in the presence of honest-but-curious adversaries and eavesdroppers; and 3) is applicable to eliminate privacy concerns for general multi-agent optimization problems in large-scale cyber-physical systems. The EV charging control is structured as a constrained optimization problem with coupled objectives and constraints, then solved in a decentralized fashion. Privacy analyses and simulations demonstrate the efficiency and efficacy of the proposed approach

A Trusted and Privacy-preserving Internet of Mobile Energy

The rapid growth in distributed energy sources on power grids leads to increasingly decentralised energy management systems for the prediction of power supply and demand and the dynamic setting of an energy price signal. Within this emerging smart grid paradigm, electric vehicles can serve as consumers, transporters, and providers of energy through two-way charging stations, which highlights a critical feedback loop between the movement patterns of these vehicles and the state of the energy grid. This paper proposes a vision for an Internet of Mobile Energy (IoME), where energy and information flow seamlessly across the power and transport sectors to enhance the grid stability and end user welfare. We identify the key challenges of trust, scalability, and privacy, particularly location and energy linking privacy for EV owners, for realising the IoME vision. We propose an information architecture for IoME that uses scalable blockchain to provide energy data integrity and authenticity, and introduces one-time keys for public EV transactions and a verifiable anonymous trip extraction method for EV users to share their trip data while protecting their location privacy. We present an example scenario that details the seamless and closed loop information flow across the energy and transport sectors, along with a blockchain design and transaction vocabulary for trusted decentralised transactions. We finally discuss the open challenges presented by IoME that can unlock significant benefits to grid stability, innovation, and end user welfare.Comment: 7 pages, 5 figure