Andreas Unterweger

Resumable Load Data Compression in Smart Grids

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.

Transparent encryption for HEVC using bit-stream-based selective coefficient sign encryption

We propose a selective encryption scheme for HEVC which allows for transparent encryption in a wide range of quantization parameters. Our approach focusses on the AC coefficient signs, since they can be altered directly in the bit stream without entropy reencoding. This allows for fast encryption and decryption while retaining full format-compliance and length-preservation. Furthermore, we show our approachs applicability for a number of use cases by evaluating the quality degradation and robustness against attacks.

Length-preserving bit-stream-based JPEG encryption

We propose a new method to encrypt baseline JPEG bit streams by selective Huffman code word swapping and coefficient value scrambling based on AES encryption. Furthermore, we show that our approach preserves the length of the bit stream while being completely format-compliant. In contrast to most existing approaches, no recompression is necessary as the encryption is applied directly to the bit stream. In addition, we assess the effort required for brute-force and known-plaintext attacks on pictures encrypted with our approach, showing that both are practically infeasible.

Building a post-compression region-of-interest encryption framework for existing video surveillance systems

We propose an encryption framework design and implementation which add region-of-interest encryption functionality to existing video surveillance systems with minimal integration and deployment effort. Apart from region-of-interest detection, all operations take place at bit-stream level and require no re-compression whatsoever. This allows for very fast encryption and decryption speed at negligible space overhead. Furthermore, we provide both objective and subjective security evaluations of our proposed encryption framework. Furthermore, we address design- and implementation-related challenges and practical concerns. These include modularity, parallelization and, most notably, the performance of state-of-the-art face detectors. We find that their performance, despite their frequent use in surveillance systems, is not insufficient for practical purposes, both in terms of speed and detection accuracy.

The Effect of Data Granularity on Load Data Compression

A vast volume of data is generated through smart metering. Suitable compression mechanisms for this kind of data are highly desirable to better utilize low-bandwidth links and to save costs and energy. To date, the important factor of data resolution has been neglected in the compression of smart meter data. In this paper, we review and evaluate compression methods for smart metering in the context of different resolutions. We show that state-of-the-art compression methods are well suited for high resolution, but not for low resolution data. Furthermore, we elaborate on the compression performance differences between appliance-level and household-level load data. We conclude that the latter are practically incompressible at most resolutions.

Slice groups for post-compression region of interest encryption in H.264/AVC and its scalable extension

Encrypting regions of interest in H.264/AVC and SVC bit streams after compression is a challenging task due to drift. In this paper, we assess whether the use of slice groups makes this task easier and what its expense in terms of bit rate overhead is. We introduce the concept of all-grey base layers for SVC which simplify the encryption of regions of interest in surveillance camera applications while obeying all standard-imposed base layer restrictions. Furthermore, we show that the use of slice groups is possible with relatively low overhead for medium and high bit rates (below 5% in most of the tested configurations). This applies to H.264/AVC as well as SVC bit streams with two and three spatial layers, including those with the newly introduced all-grey base layers. Although we are able to contain spatial and inter-layer drift with our proposed encryption setup, temporal drift still remains an issue that cannot be solved by sole usage of slice groups. HighlightsFor medium and high bit rates, slice groups reduce drift with low overhead.The introduced all-grey base layer simplifies encryption by avoiding drift.Containing inter-layer drift can be reduced to containing temporal drift.

Region of interest signalling for encrypted JPEG images

We propose and evaluate different methods to signal position and size of encrypted RoIs (Regions of Interest) in JPEG images. After discussing various design choices regarding the encoding of RoI coordinates with a minimal amount of bits, we discuss both, existing and newly proposed approaches to signal the encoded coordinates inside JPEG images. By evaluating the different signalling methods on various data sets, we show that several of our proposed encoding methods outperform JBIG in this special use case. Furthermore, we show that one of our proposed signalling methods allows length-preserving lossless signalling, i.e., storing RoI coordinates in a format-compliant way inside the JPEG images without quality loss or change of file size.

Privacy-preserving blockchain-based electric vehicle charging with dynamic tariff decisions

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.

Short-term memory for self-collecting mutators

We propose a new memory model called short-term memory for managing objects on the heap. In contrast to the traditional persistent memory model for heap management, objects in short-term memory expire after a finite amount of time, which makes deallocation unnecessary. Instead, expiration of objects may be extended, if necessary, by refreshing. We have developed a concurrent, incremental, and non-moving implementation of short-term memory for explicit refreshing called self-collecting mutators that is based on programmer-controlled time and integrated into state-of-the-art runtimes of three programming languages: C, Java, and Go. All memory management operations run in constant time without acquiring any locks modulo the underlying allocators. Our implementation does not require any additional heap management threads, hence the name. Expired objects may be collected anywhere between one at a time for maximal incrementality and all at once for maximal throughput and minimal memory consumption. The integrated systems are heap management hybrids with persistent memory as default and short-term memory as option. Our approach is fully backwards compatible. Legacy code runs without any modifications with negligible runtime overhead and constant per-object space overhead. Legacy code can be modified to take advantage of short-term memory by having some but not all objects allocated in short-term memory and managed by explicit refreshing. We study single- and multi-threaded use cases in all three languages macro-benchmarking C and Java and micro-benchmarking Go. Our results show that using short-term memory (1) simplifies heap management in a state-of-the-art H.264 encoder written in C without additional time and minor space overhead, and (2) improves, at the expense of safety, memory management throughput, latency, and space consumption by reducing the number of garbage collection runs, often even to zero, for a number of Java and Go programs.

Privacy-Preserving Smart Grid Tariff Decisions with Blockchain-Based Smart Contracts

The smart grid changes the way how energy and information are exchanged and offers opportunities for incentive-based load balancing. For instance, customers may shift the time of energy consumption of household appliances in exchange for a cheaper energy tariff. This paves the path towards a full range of modular tariffs and dynamic pricing that incorporate the overall grid capacity as well as individual customer demands. This also allows customers to frequently switch within a variety of tariffs from different utility providers based on individual energy consumption and provision forecasts. For automated tariff decisions it is desirable to have a tool that assists in choosing the optimum tariff based on a prediction of individual energy need and production. However, the revelation of individual load patterns for smart grid applications poses severe privacy threats for customers as analyzed in depth in literature. Similarly, accurate and fine-grained regional load forecasts are sensitive business information of utility providers that are not supposed to be released publicly. This paper extends previous work in the domain of privacy-preserving load profile matching where load profiles from utility providers and load profile forecasts from customers are transformed in a distance-preserving embedding in order to find a matching tariff. The embeddings neither reveal individual contributions of customers nor those of utility providers. Prior work requires a dedicated entity that needs to be trustworthy at least to some extent for determining the matches. In this paper we propose an adaption of this protocol, where we use blockchains and smart contracts for this matching process, instead. Blockchains are gaining widespread adaption in the smart grid domain as a powerful tool for public commitments and accountable calculations. The use of a blockchain for this protocol makes the calculations for tariff matching public, while still maintaining the privacy through embeddings. Further, such decentralized and trust-free blockchains improve the existing solution in terms of verifiability, reliability, and transparency.