Electric vehicles are gaining widespread adoption and are a key component in the establishment of the smart grid. Beside the increasing number of electric vehicles, a dense and widespread charging infrastructure will be required. This offers the opportunity for a broad range of different energy providers and charging station operators, both of which can offer energy at different prices depending on demand and supply. While customers benefit from a liberalized market and a wide selection of tariff options, such dynamic pricing use cases are subject to privacy issues and allow to detect the customer's position and to track vehicles for, e.g., targeted advertisements. In this paper we present a reliable, automated and privacy-preserving selection of charging stations based on pricing and the distance to the electric vehicle. The protocol builds on a blockchain where electric vehicles signal their demand and charging stations send bids similar to an auction. The electric vehicle owner then decides on a particular charging station based on the supply-side offers it receives. This paper shows that the use of blockchains increases the reliability and the transparency of this approach while preserving the privacy of the electric vehicle owners.
The collection of detailed consumption data through smart metering has led to privacy concerns. Aggregating the consumption data over a number of smart meters can be used to strike a balance between functional and privacy requirements. A number of contributions have proposed the use of differential privacy in smart metering to perturb aggregates in order to provide a proven privacy property for end consumers. However, as differential privacy has originally been proposed for very large datasets, the applicability in real-world smart metering is not guaranteed. In this paper, the effect of differential privacy on real smart metering data is studied, especially with respect to balancing utility and privacy requirements. The main finding is that even after some improvements of the basic method the aggregation group size must be of the order of thousands of smart meters in order to have reasonable utility.
We propose a compression approach for load profile data, which addresses practical requirements of smart metering. By providing linear time complexity with respect to the input data size, our compression approach is suitable for low-complexity encoding and decoding for storage and transmission of load profile data in smart grids. Furthermore, it allows for resumability with very low overhead on error-prone transmission lines, which is an important feature not available for standard time series compression schemes. In terms of compression efficiency, our approach outperforms transmission encodings that are currently used for electricity metering by an order of magnitude.
Image and video encryption has become a widely discussed topic; especially for the fully featured JPEG2000 compression standard numerous approaches have been proposed. A comprehensive survey of state-of-the-art JPEG2000 encryption is given. JPEG2000 encryption schemes are assessed in terms of security, runtime and compression performance and their suitability for a wide range of application scenarios.
We propose a framework to encrypt Baseline JPEG files directly at bitstream level, i.e., without the need to recompress them. Our approach enables encrypting more than 25 pictures per second in VGA resolution, allowing real-time operation in typical video surveillance applications. In addition, our approach preserves the length of the bitstream while being completely format-compliant. Furthermore, we show that an attack on the encryption process, which partly relies on AES, is practically infeasible.
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