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Abstract This work introduces , an anonymization pipeline that can be used for generalization and suppression-based anonymization of nominal textual tabular data. It automatically generates value generalization hierarchies (VGHs) that, in turn, can be used to generalize attributes in quasi-identifiers. The pipeline leverages embeddings to generate semantically close value generalizations through iterative clustering. We applied KMeans and Hierarchical Agglomerative Clustering on 13 different predefined text embeddings (both open and closed-source (via APIs)). Our approach is experimentally tested on a well-known benchmark dataset for anonymization: The UCI Machine Learning Repository’s Adult dataset. supports anonymization procedures by offering more possibilities compared to using arbitrarily chosen VGHs. Experiments demonstrate that these VGHs can outperform manually constructed ones in terms of downstream efficacy (especially for small k -anonymity) and therefore can foster the quality of anonymized datasets. Our implementation is made public.

Due to the increasing privacy demand in data processing, Fully Homomorphic Encryption (FHE) has recently received growing attention for its ability to perform calculations over encrypted data. Since the data can be processed in encrypted form and the output remains encrypted, only an authorized user or a user who holds the key can decrypt the data and understand its meaning. Hence, it is possible to securely outsource data processing to untrustworthy but powerful public computing resources on the edge. However, due to the high computational complexity, FHE-based data processing experiences scalability related concerns. It is currently unclear whether FHE can be used to solve large-scale problems. In this paper, we propose a novel general distributed FHE-based data processing approach as a concrete step towards solving the scalability challenge. The main idea behind our approach is to use slightly more communication overhead for a shorter computing circuit in FHE, hence, reducing the overall complexity. We verify our new model’s efficiency and effectiveness by comparing the distributed approach with the central approach over various FHE schemes (CKKS, BGV, and BFV). This is performed using one of the most popular libraries of FHE ‘‘Microsoft SEAL by performing specific mathematical operations and observing the time consumed. The empirical results demonstrate that the proposed approach results in a significant reduction in time, up to 54% compared to the traditional central approach.

To realize frustration-aware in-vehicle systems based on real-time user monitoring, personal data have to be recorded, analyzed and (potentially) stored raising data privacy concerns that may reduce the user acceptance and hence the spread of such systems. Complementing the development of a frustration-aware system with voice interface in the project F-RELACS, a data privacy concept was created based on the principles privacy by design and privacy by default recommended in the European General Data Protection Regulation. Nine criteria were formulated and 23 concrete measures to satisfy the criteria were derived. The measures were evaluated in an online study with 96 participants between 18 and 74 years. On average, the measures were rated as rather sufficient to sufficient. Participants evaluated the use of commercial third-party software for speech processing as most critical. All results are discussed and proposals to further increase the acceptance of frustration-aware systems are outlined.

The connectivity of embedded systems is increasing accompanied with thriving technology such as Internet of Things/Everything (IoT/E), Connected Cars, Smart Cities, Industry 4.0, 5G or Software-Defined Everything. Apart from the benefits of these trends, the continuous networking offers hackers a broad spectrum of attack vectors. The identification of attacks or unknown behavior through Intrusion Detection Systems (IDS) has established itself as a conducive and mandatory mechanism apart from the protection by cryptographic schemes in a holistic security eco-system. In systems where resources are valuable goods and stand in contrast to the ever increasing amount of network traffic, sampling has become a useful utility in order to detect malicious activities on a manageable amount of data. In this work an algorithm – Uncoupled MAC – is presented which secures network communication through a cryptographic scheme by uncoupled Message Authentication Codes (MAC) but as a side effect also provides IDS functionality producing alarms based on the violation of Uncoupled MAC values. Through a novel self-regulation extension, the algorithm adapts it’s sampling parameters based on the detection of malicious actions. The evaluation in a virtualized environment clearly shows that the detection rate increases over runtime for different attack scenarios. Those even cover scenarios in which intelligent attackers try to exploit the downsides of sampling.

In the emerging IoT ecosystem in which the internetworking will reach a totally new dimension the crucial role of efficient security solutions for embedded devices will be without controversy. Typically IoT-enabled devices are equipped with integrated circuits, such as ASICs or FPGAs to achieve highly specific tasks. Such devices must have cryptographic layers implemented and must be able to access cryptographic functions for encrypting/decrypting and signing/verifying data using various algorithms and generate true random numbers, random primes, and cryptographic keys. In the context of a limited amount of resources that typical IoT devices will exhibit, due to energy efficiency requirements, efficient hardware structures in terms of time, area, and power consumption must be deployed. In this paper, we describe a scalable word-based multivendor-capable cryptographic core, being able to perform arithmetic operations in prime and binary extension finite fields based on Montgomery Arithmetic. The functional range comprises the calculation of modular additions and subtractions, the determination of the Montgomery Parameters, and the execution of Montgomery Multiplications and Montgomery Exponentiations. A prototype implementation of the adaptable arithmetic core is detailed. Furthermore, the decomposition of cryptographic algorithms to be used together with the proposed core is stated and a performance analysis is given.

Nowadays embedded systems in many application areas such as automotive, medical and industrial automation are designed with well-defined hardware and software components which are not meant to be exposed for user modifications. Adding or removing components to/from such systems is not permitted and sometimes not even possible since the systems often have to be up and running in a 24/7 manner. However due to the well-known nature of these types of embedded platform configuration the effort an attacker has to invest usually is reduced. The proposed publication presents a defense in depth strategy for application specific embedded devices by combining hardware-based security enhancements of modern processors with hardware security modules.