Privacy Strategies in Smart Metering Source: http://abstrusegoose.com/553 Tobias Klenze: Privacy Strategies in Smart Metering 1
Privacy Strategies in Smart Metering Tobias Klenze Chair for Network Architectures and Services Department for Computer Science Technische Universit¨ at M¨ unchen June 13, 2014 Tobias Klenze: Privacy Strategies in Smart Metering 2
Outline 1 Smart Meters Real-time data aggregation 2 Billing protocols 3 Conclusions 4 Tobias Klenze: Privacy Strategies in Smart Metering 3
Outline 1 Smart Meters Real-time data aggregation 2 Billing protocols 3 Conclusions 4 Tobias Klenze: Privacy Strategies in Smart Metering 4
What’s a “Smart Meter”? Normal Meter Smart Meter Kristoferb, CC-BY-SA 3.0 EVB Energy, CC-BY-SA 3.0 Tobias Klenze: Privacy Strategies in Smart Metering 5
(Supposed) Advantages of Smart Meters Smart Meters! What are they good for? 1 Automated billing (no physical inspections) 2 Complex tariffs (e.g. electricity costs more in peak times) 3 “Smart” devices take tariffs into account 4 Grid / plant management – forecasting of consumption demand 5 Leak detection Tobias Klenze: Privacy Strategies in Smart Metering 6
Smart Meter Deployment EU directive: 80% deployment by 2020. Investments: 3.15 billion Euro in the EU already. Smart Meters will come to you as well Tobias Klenze: Privacy Strategies in Smart Metering 7
Smart Meter Deployment EU directive: 80% deployment by 2020. Investments: 3.15 billion Euro in the EU already. Smart Meters will come to you as well Tobias Klenze: Privacy Strategies in Smart Metering 7
The Problem: High-frequency data Readings intervals Normal Meters: once per month. Low frequency, no problem Smart Meters: once per 15 minutes. High frequency, problematic Tobias Klenze: Privacy Strategies in Smart Metering 8
The Problem: High-frequency data Readings intervals Normal Meters: once per month. Low frequency, no problem Smart Meters: once per 15 minutes. High frequency, problematic Source: Private Memoirs of a Smart Meter, 2010, Molina-Markham, Shenoy, Prashant et al Tobias Klenze: Privacy Strategies in Smart Metering 8
Outline 1 Smart Meters Real-time data aggregation 2 Billing protocols 3 Conclusions 4 Tobias Klenze: Privacy Strategies in Smart Metering 9
Aggregating real-time data Goal: Provide real-time usage data to grid & utility provider AND respect privacy. Problem: Providers poorly define what data they need. Tobias Klenze: Privacy Strategies in Smart Metering 10
Approaches for real-time data sharing Idea: Preprocess (obfuscate) consumption data. Quantization works well, Down-sampling not as much. Problem: Still some predictions are possible. Idea: Aggregate data of different households (without trusted third party). Tobias Klenze: Privacy Strategies in Smart Metering 11
Approaches for real-time data sharing Idea: Preprocess (obfuscate) consumption data. Quantization works well, Down-sampling not as much. Problem: Still some predictions are possible. Idea: Aggregate data of different households (without trusted third party). Tobias Klenze: Privacy Strategies in Smart Metering 11
Homomorphic encryption Homomorphic encryption = Messing with ciphertext Allows modification of ciphertext yielding meaningful ciphertext. We use: enc ( m 1 ) ∗ enc ( m 2 ) = enc ( m 1 + m 2 ) Tobias Klenze: Privacy Strategies in Smart Metering 12
Protocol flow (1) Record consumption m 1 M 1 Local M 4 M 2 Station M 3 Tobias Klenze: Privacy Strategies in Smart Metering 13
Protocol flow (2) Choose random a j, 1 s.t. m 1 = a 1 , 1 + a 2 , 1 + a 3 , 1 + a 4 , 1 M 1 Local M 4 M 2 Station M 3 Tobias Klenze: Privacy Strategies in Smart Metering 14
Protocol flow (3) For all j enc pk j ( a 1 j ) M 1 Local M 4 M 2 Station M 3 Tobias Klenze: Privacy Strategies in Smart Metering 15
Protocol flow (4) M 1 enc pk j ( a 1 j ) Local M 4 M 2 Station M 3 Tobias Klenze: Privacy Strategies in Smart Metering 16
Protocol flow (5) M 1 Local M 4 M 2 Station For all i , j : enc pk j ( a ij ) M 3 Tobias Klenze: Privacy Strategies in Smart Metering 17
Protocol flow (6) M 1 Local M 4 M 2 Station For all j : � enc pk j ( a ij ) i � = j = enc pk j ( � a ij ) M 3 i � = j Tobias Klenze: Privacy Strategies in Smart Metering 18
Protocol flow (7) M 1 Local M 4 M 2 Station enc pk 3 ( � a i 3 ) i � =3 M 3 Tobias Klenze: Privacy Strategies in Smart Metering 19
Protocol flow (8) M 1 Local M 4 M 2 Station M 3 dec sk 3 enc pk 3 ( � a i 3 ) i � =3 Tobias Klenze: Privacy Strategies in Smart Metering 20
Protocol flow (9) M 1 Local M 4 M 2 Station r 3 := � a i 3 + a 33 i � =3 M 3 Tobias Klenze: Privacy Strategies in Smart Metering 21
Protocol flow (10) M 1 Local M 4 M 2 Station � j r j M 3 Tobias Klenze: Privacy Strategies in Smart Metering 22
Outline 1 Smart Meters Real-time data aggregation 2 Billing protocols 3 Conclusions 4 Tobias Klenze: Privacy Strategies in Smart Metering 23
Privacy-sensitive Billing protocols Security in the protocol is a must Ideally: Allows for different tariffs No centralized trust – neither the consumer can cheat, nor the utility Utility has no direct access on smart meter No physical inspections required → Complex problem but protocols exist. Tobias Klenze: Privacy Strategies in Smart Metering 24
Outline 1 Smart Meters Real-time data aggregation 2 Billing protocols 3 Conclusions 4 Tobias Klenze: Privacy Strategies in Smart Metering 25
Summary & Conclusions Lessons learned: 1 Privacy is a big problem with current smart meters. 2 Protocols for real-time data and secure billing . 3 Privacy in Smart Meters is possible. Tobias Klenze: Privacy Strategies in Smart Metering 26
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