# Local Differential Privacy for Smart Meter Data Sharing

arXiv: https://arxiv.org/abs/2311.04544 · HTML: https://mlsanity.com/paper/2311.04544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Continuous Release of Data Streams under both Centralized and Local Differential Privacy](https://mlsanity.com/paper/2005.11753/) (2020)
- [Locally Differentially Private Heavy Hitter Identification](https://mlsanity.com/paper/1708.06674/) (2017)
- [Local Differential Privacy for Federated Learning](https://mlsanity.com/paper/2202.06053/) (2022)
- [LDP-IDS: Local Differential Privacy for Infinite Data Streams](https://mlsanity.com/paper/2204.00526/) (2022)
- [Performance Evaluation of Differential Privacy Mechanisms in Blockchain based Smart Metering](https://mlsanity.com/paper/2007.09802/) (2020)

## Abstract (arXiv)

Energy disaggregation techniques, which use smart meter data to infer appliance energy usage, can provide consumers and energy companies valuable insights into energy management. However, these techniques also present privacy risks, such as the potential for behavioral profiling. Local differential privacy (LDP) methods provide strong privacy guarantees with high efficiency in addressing privacy concerns. However, existing LDP methods focus on protecting aggregated energy consumption data rather than individual appliances. Furthermore, these methods do not consider the fact that smart meter data are a form of streaming data, and its processing methods should account for time windows. In this paper, we propose a novel LDP approach (named LDP-SmartEnergy) that utilizes randomized response techniques with sliding windows to facilitate the sharing of appliance-level energy consumption data over time while not revealing individual users' appliance usage patterns. Our evaluations show that LDP-SmartEnergy runs efficiently compared to baseline methods. The results also demonstrate that our solution strikes a balance between protecting privacy and maintaining the utility of data for effective analysis.