Deploying and Evaluating Pufferfish Privacy for Smart Meter Data (Technical Report)

Information hiding ensures privacy by transforming personalized data so that certain sensitive information cannot be inferred any more. One state-of-the-art information-hiding approach is the Pufferfish framework. It lets the users specify their privacy requirements as so-called discriminative pairs of secrets, and it perturbs data so that an adversary does not learn about the probability distribution of such pairs. However, deploying the framework on complex data such as time series requires application specific work. This includes a general definition of the representation of secrets in the data. Another issue is that the tradeoff between Pufferfish privacy and utility of the data is largely unexplored in quantitative terms. In this study, we quantify this tradeoff for smart meter data. Such data contains fine-grained time series of power-consumption data from private households. Disseminating such data in an uncontrolled way puts privacy at risk. We investigate how time series of energy consumption data must be transformed to facilitate specifying secrets that Pufferfish can use. We ensure the generality of our study by looking at different information-extraction approaches, such as re-identification and non-intrusive-appliance-load monitoring, in combination with a comprehensive set of secrets. Additionally, we provide quantitative utility results for a real-world application, the so-called local energy market

Implications of Data Sampling Resolution on Water Use Simulation, End-Use Disaggregation, And Demand Management

Understanding the tradeoff between the information of high-resolution water use data and the costs of smart meters to collect data with sub-minute resolution is crucial to inform smart meter networks. To explore this tradeoff, we first present STREaM, a STochastic Residential water End-use Model that generates synthetic water end-use time series with 10-s and progressively coarser sampling resolutions. Second, we apply a comparative framework to STREaM output and assess the impact of data sampling resolution on end-use disaggregation, post meter leak detection, peak demand estimation, data storage, and meter availability. Our findings show that increased sampling resolution allows more accurate end-use disaggregation, prompt water leakage detection, and accurate and timely estimates of peak demand. Simultaneously, data storage requirements and limited product availability mean most large-scale, commercial smart metering deployments sense data with hourly, daily, or coarser sampling frequencies. Overall, this work provides insights for further research and commercial deployment of smart water meters

Deploying and Evaluating Pufferfish Privacy for Smart Meter Data (Technical Report \u2715)

Information hiding ensures privacy by transforming personalized data so that certain sensitive information cannot be inferred any more. One state-of-the-art information-hiding approach is the Pufferfish framework. It lets the users specify their privacy requirements as so-called discriminative pairs of secrets, and it perturbs data so that an adversary does not learn about the probability distribution of such pairs. However, deploying the framework on complex data such as time series requires application specific work. This includes a general definition of the representation of secrets in the data. Another issue is that the tradeoff between Pufferfish privacy and utility of the data is largely unexplored in quantitative terms. In this study, we quantify this tradeoff for smart meter data. Such data contains fine-grained time series of power-consumption data from private households. Disseminating such data in an uncontrolled way puts privacy at risk. We investigate how time series of energy consumption data must be transformed to facilitate specifying secrets that Pufferfish can use. We ensure the generality of our study by looking at different information-extraction approaches, such as re-identification and non-intrusive-appliance-load monitoring, in combination with a comprehensive set of secrets. Additionally, we provide quantitative utility results for a real-world application, the so-called local energy market

Privacy-Enhancing Methods for Time Series and their Impact on Electronic Markets

The amount of collected time series data containing personal information has increased in the last years, e.g., smart meters store time series of power consumption data. Using such data for the benefit of society requires methods to protect the privacy of individuals. Those methods need to modify the data. In this thesis, we contribute a provable privacy method for time series and introduce an application specific measure in the smart grid domain to evaluate their impact on data quality

An Open Source Cyberinfrastructure for Collecting, Processing, Storing and Accessing High Temporal Resolution Residential Water Use Data

Collecting and managing high temporal resolution residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. We developed an open-source, modular, generalized architecture called Cyberinfrastructure for Intelligent Water Supply (CIWS) to automate the process from data collection to analysis and presentation of high temporal residential water use data. A prototype implementation was built using existing open-source technologies, including smart meters, databases, and services. Two case studies were selected to test functionalities of CIWS, including push and pull data models within single family and multi-unit residential contexts, respectively. CIWS was tested for scalability and performance within our design constraints and proved to be effective within both case studies. All CIWS elements and the case study data described are freely available for re-use

Advancing Data Collection, Management, and Analysis for Quantifying Residential Water Use via Low Cost, Open Source, Smart Metering Infrastructure

Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. Understanding how water is used at the household level is vital for this objective.Water meters measure water use for billing purposes, commonly at a monthly, or coarser temporal resolutions. This is insufficient to understand where water is used (i.e., the distribution of water use across different fixtures like toilets, showers, outdoor irrigation), when water is used (i.e., identifying peaks of consumption, instantaneous or at hourly, daily, weekly intervals), the efficiency of water using fixtures, or water use behaviors across different households. Most smart meters available today are not capable of collecting data at the temporal resolutions needed to fully characterize residential water use, and managing this data represents a challenge given the rapidly increasing volume of data generated. The research in this dissertation presents low cost, open source cyberinfrastructure (datalogging and data management systems) to collect and manage high temporal resolution, residential water use data. Performance testing of the cyberinfrastructure demonstrated the scalability of the system to multiple hundreds of simultaneous data collection devices. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah where we found significant variability in the temporal distribution, timing, and volumes of indoor water use. This variability can impact the design of water conservation programs, estimations and forecast of water demand, and sizing of future water infrastructure. Outdoor water use was the largest component of residential water use, yet homeowners were not significantly overwatering their landscapes. Opportunities to improve the efficiency of water using fixtures and to conserve water by promoting behavior changes exist among participants