  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.
