Authors Muhammad Rizwan Asghar Gyrgy Dn Daniele Miorandi

Authors: Muhammad Rizwan Asghar, György Dán, Daniele Miorandi, Imrich Chlamtac Source: IEEE Communications Surveys & Tutorials, Vol. 19, pp. 2820 -2835, 27 Jun 2017 Speaker: Kai. Fan Chien Date: 3/16/2019

§ 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 2

§ Smart grid Ø Demand response Ø Optimise the supply of electricity § Smart meters Ø Real-time Ø Primary sources of data 3

§ Data collected by smart meters may also serve for invading consumers’ privacy. § In this survey Ø Regulatory and policy context Ø Overview of state-of-the-art solutions Ø Provide recommendations 1. 2. 3. Billing Operations Alue-added services 4

§ Automated meters and smart meters Ø Consumption of electric energy with a variable time granularity Ø Meter Data Management System (MDMS) Ø Receive pricing information and load control commands Ø Exchange information with smart home appliances § Intervals of 15 minutes 5

§ Different domains of the smart metering infrastructure Ø Customer Ø Communication Ø MDMS Ø Data 6

§ Billing, Operations, and Value-Added Services Ø Billing Not be real-time Ø Operations Real-time ü The second use of smart meter data ü State Estimation (SE) / Volt and Var Control (VVC) / Fault Location, Isolation and Service Restoration (FLISR) Ø Value-Added Services Real-time or Batch 7

§ Automated and smart meters collect personal data. Ø Greveler et al. fine enough measurements could reveal consumers’ interests as well 8

§ Privacy legislation for smart meter data Ø EU ü Data are necessary ü Cannot be used for a different purpose Ø NIST 9

§ Two notions of privacy Ø Cryptographic privacy Ø Statistical privacy ü Differential privacy ü k -anonymity 10

§ Requirements for privacy-preserving protocols for smart meter data management Ø Confidentiality Ø Integrity Ø Authenticity Ø Non-Repudiation Ø Auditability 11

§ Preserving privacy depends significantly on the attacker model Ø Honest-but-curious, also called semi-honest Ø Malicious attacker 12

§ Summary of the problems, existing solutions and remaining research issues under the trusted operator model 13

§ Tamper-resistance of smart meters Ø Trusted Platform Module (TPM) § Electricity theft Ø Mc. Laughlin, Podkuiko and Mc. Daniel describe methods, including password extraction and storage tampering Ø Automated meters for electricity and for gas were recently found tampered within the U. K 14

§ Data confidentiality and trust models Ø Public Key Infrastructure (PKI) Ø Baumeister investigated what PKI architecture would be most suitable to meet the requirements Ø Main issue with the PKI is efficient certificate revocation Ø By adding random noise to the data ? ? ? 15

§ Consent and Access Control Ø Mandatory Access Control (MAC). Ø Discretionary Access Control (DAC). Ø Role-Based Access Control (RBAC). Ø e. Xtensible Access Control Markup Language (XACML) § Data Integrity and Auditing 16

§ Summary of the issues, privacy-preserving solutions and research directions discussed 17

§ Provides a detailed comparative analysis of privacy-preserving solutions for smart meter data • • • Billing (BL) Operations (OP) Value-Added Services (VAS) • • Are (c)onfidentiality (i)ntegrity (AUTH)etication No(NM)alleability • • No(NR)epudiation (AUD)itability (ANO)onymity (SY)bil Attack 18

§ Privacy-Preserving Billing Ø Filtering With Energy Storage for Statistical Privacy ü The energy storage protects customers’ privacy by hiding the use of individual appliances Ø Secure Computation for Cryptographic Privacy ü Jawurek et al. propose a scheme based on Pedersen commitments ü Non-Interactive Zero-Knowledge proof (NIZK) ü Anonymous credential system 19

§ Privacy-preserving operations Ø With a trusted third party ü Aggregation algorithms for cryptographic privacy Ø Without a trusted third party ü Homomorphic encryption ü Providing Statistical Privacy ü Privacy Economics 20

§ Value-Added Services Ø Demand-response Ø Identifying appliance level anomalies Ø Optimise the electricity consumption of a household 21

§ Privacy-preserving meter data delivery and management § Meter data collection for the three application areas Ø Billing Ø Operations Ø Alue-added services § Trusted Operator Model and non-Trusted Operator Model 22