Abstract. Traditional electricity meters are replaced by Smart Meters in customers' households. Smart Meters collect fine-grained utility consumption profiles from customers, which in turn enables the introduction of dynamic, time-of-use tariffs. However, the fine-grained usage data that is compiled in this process also allows to infer the inhabitant's personal schedules and habits. We propose a privacy-preserving protocol that enables billing with time-of-use tariffs without disclosing the actual consumption profile to the supplier. Our approach relies on a zero-knowledge proof based on Pedersen Commitments performed by a plug-in privacy component that is put into the communication link between Smart Meter and supplier's back-end system. We require no changes to the Smart Meter hardware and only small changes to the software of Smart Meter and back-end system. In this paper we describe the functional and privacy requirements, the specification and security proof of our solution and give a performance evaluation of a prototypical implementation.

Consumption traces collected by Smart Meters are highly privacy sensitive data. For this reason, current best practice is to store and process such data in pseudonymized form, separating identity information from the consumption traces. However, even the consumption traces alone may provide many valuable clues to an attacker, if combined with limited external indicators. Based on this observation, we identify two attack vectors using anomaly detection and behavior pattern matching that allow effective depseudonymization. Using a practical evaluation with reallife consumption traces of 53 households, we verify the feasibility of our techniques and show that the attacks are robust against common countermeasures, such as resolution reduction or frequent re-pseudonymization.

Abstract. Real-time statistics on smart meter consumption data must preserve consumer privacy and tolerate smart meter failures. Existing protocols for this private distributed aggregation model suffer from various drawbacks that disqualify them for application in the smart energy grid. Either they are not fault-tolerant or if they are, then they require bidirectional communication or their accuracy decreases with an increasing number of failures. In this paper, we provide a protocol that fixes these problems and furthermore, supports a wider range of exchangeable statistical functions and requires no group key management. A key-managing authority ensures the secure evaluation of authorized functions on fresh data items using logical time and a custom zero-knowledge proof providing differential privacy for an unbounded number of statistics calculations. Our privacy-preserving protocol provides all the properties that make it suitable for use in the smart energy grid.

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