Smart Meter Data Privacy: A Survey Authors: Muhammad Rizwan Asghar, György Dán, Daniele Miorandi, Imrich Chlamtac Source: IEEE Communications Surveys & Tutorials, Vol. 19, pp. 2820-2835, 27 Jun 2017 Speaker: KaiFan Chien Date: 3/16/2019

Outline Introduction Smart Meter Data and Privacy Requirements of Privacy and Security Privacy Protection for Smart Meter Data under the Trusted Operator Model Service-Specific Privacy Protection Under the Non-Trusted Operator Model Conclusions

Introduction(1/2) Smart grid Smart meters Demand response Real-time Optimise the supply of electricity Primary sources of data

Introduction(2/2) Data collected by smart meters may also serve for invading consumers’ privacy. In this survey Regulatory and policy context Overview of state-of-the-art solutions Provide recommendations Billing Operations Alue-added services

Smart Meter Data and Privacy(1/4) Automated meters and smart meters Consumption of electric energy with a variable time granularity Meter Data Management System (MDMS) Receive pricing information and load control commands Exchange information with smart home appliances Intervals of 15 minutes

Smart Meter Data and Privacy(2/4) Different domains of the smart metering infrastructure Customer Communication MDMS Data

Smart Meter Data and Privacy(3/4) Billing, Operations, and Value-Added Services Billing  Not be real-time Operations  Real-time The second use of smart meter data State Estimation (SE) / Volt and Var Control (VVC) / Fault Location, Isolation and Service Restoration (FLISR) Value-Added Services  Real-time or Batch

Smart Meter Data and Privacy(4/4) Automated and smart meters collect personal data. Greveler et al. fine enough measurements could reveal consumers’ interests as well

Requirements of Privacy and Security(1/4) Privacy legislation for smart meter data EU Data are necessary Cannot be used for a different purpose NIST

Requirements of Privacy and Security(2/4) Two notions of privacy Cryptographic privacy Statistical privacy Differential privacy k -anonymity

Requirements of Privacy and Security(3/4) Requirements for privacy-preserving protocols for smart meter data management Confidentiality Integrity Authenticity Non-Repudiation Auditability

Requirements of Privacy and Security(4/4) Preserving privacy depends significantly on the attacker model Honest-but-curious, also called semi-honest Malicious attacker

Privacy Protection for Smart Meter Data under the Trusted Operator Model(1/4) Summary of the problems, existing solutions and remaining research issues under the trusted operator model

Privacy Protection for Smart Meter Data under the Trusted Operator Model(2/4) Tamper-resistance of smart meters Trusted Platform Module (TPM) Electricity theft McLaughlin, Podkuiko and McDaniel describe methods, including password extraction and storage tampering Automated meters for electricity and for gas were recently found tampered within the U.K

Privacy Protection for Smart Meter Data under the Trusted Operator Model(3/4) Data confidentiality and trust models Public Key Infrastructure (PKI) Baumeister investigated what PKI architecture would be most suitable to meet the requirements Main issue with the PKI is efficient certificate revocation By adding random noise to the data ???

Privacy Protection for Smart Meter Data under the Trusted Operator Model(4/4) Consent and Access Control Mandatory Access Control (MAC). Discretionary Access Control (DAC). Role-Based Access Control (RBAC). eXtensible Access Control Markup Language (XACML) Data Integrity and Auditing

Service-Specific Privacy Protection Under the Non-Trusted Operator Model(1/5) Summary of the issues, privacy-preserving solutions and research directions discussed

Service-Specific Privacy Protection Under the Non-Trusted Operator Model(2/5) Provides a detailed comparative analysis of privacy-preserving solutions for smart meter data Are (c)onfidentiality (i)ntegrity (AUTH)etication No(NM)alleability No(NR)epudiation (AUD)itability (ANO)onymity (SY)bil Attack Billing (BL) Operations (OP) Value-Added Services (VAS)

Service-Specific Privacy Protection Under the Non-Trusted Operator Model(3/5) Privacy-Preserving Billing Filtering With Energy Storage for Statistical Privacy The energy storage protects customers’ privacy by hiding the use of individual appliances Secure Computation for Cryptographic Privacy Jawurek et al. propose a scheme based on Pedersen commitments Non-Interactive Zero-Knowledge proof (NIZK) Anonymous credential system

Service-Specific Privacy Protection Under the Non-Trusted Operator Model(4/5) Privacy-preserving operations With a trusted third party Aggregation algorithms for cryptographic privacy Without a trusted third party Homomorphic encryption Providing Statistical Privacy Privacy Economics

Service-Specific Privacy Protection Under the Non-Trusted Operator Model(5/5) Value-Added Services Demand-response Identifying appliance level anomalies Optimise the electricity consumption of a household

Conclusions Privacy-preserving meter data delivery and management Meter data collection for the three application areas Billing Operations Alue-added services Trusted Operator Model and non-Trusted Operator Model