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Item type:Publication, Access status: without files , Unlinkable data sharing with dynamic access control(Association for Computing Machinery (ACM), 2025-03); ; In an increasingly information-driven society, the volume of digital footprints left by individuals has surged significantly. Safeguarding the anonymity of data generated by computing devices is becoming more challenging as these offer deep insights into personal behaviors. We propose a user-centric and privacy-preserving data space for unlinkable data sharing based on a central intermediary. By integrating differential privacy techniques with fine-grained access control, our system allows data providers to store their data confidentially and unlinkable at the intermediary. Data consumers can then locate and request data via this intermediary, ensuring that data providers remain informed without revealing the origin of the data. Additionally, the intermediary facilitates continuous data sharing, requiring only a single data upload. Our approach is designed to protect data providers from both external and internal attackers, as well as from an honest-but-curious intermediary.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , EdgeMiner: distributed process mining at the data sources(Association for Computing Machinery (ACM), 2025-05-14); ; ; ; Process mining is moving beyond mining traditional event logs and nowadays includes, for example, data sourced from sensors in the Internet of Things (IoT). The volume and velocity of data generated by such sensors make it increasingly challenging to efficiently process the data by traditional process discovery algorithms, which operate on a centralized event log. This paper presents EdgeMiner, an algorithm for distributed process mining operating directly on sensor nodes on a stream of real-time event data. In contrast to centralized algorithms, EdgeMiner tracks each event and its predecessor and successor events directly on the sensor node where the event is sensed and recorded. As EdgeMiner aggregates direct successions on the individual nodes, the raw data does not need to be stored centrally, thus improving scalability and privacy. We analytically and experimentally show the correctness of EdgeMiner. Our evaluation results show that EdgeMiner determines predecessors for each event efficiently, reducing the communication overhead by up to 96% compared to querying all nodes. Further, we show that the number of queried nodes stabilizes after relatively few events, and batching predecessor queries in groups reduces the average queried nodes per event to less than 2.5%.Publicationtype: Conference Paper13 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , SeismicSense: phase picking of seismic events with embedded machine learning(Association for Computing Machinery (ACM), 2025-03); ; ; Analyzing seismic data is essential for understanding natural geological processes and anthropogenic activities, particularly in localizing seismic events. While recent advances in seismic analysis rely heavily on resource-intensive machine learning approaches, these methods are impractical in resource-constrained environments such as underwater, underground, or rural areas. To address this, we introduce SeismicSense, a lightweight neural network (NN)-based solution for sensor-level seismic data analysis. SeismicSense detects seismic events and localizes them by identifying seismic event phases through a cascading architecture. Initially, SeismicSense uses an NN to filter out non-earthquake events, minimizing false positives. Upon identifying an earthquake, it detects the P- and S- phases, which are crucial for determining the origin and magnitude of seismic activity. SeismicSense significantly reduces data transmission by communicating only the arrival times of these phases to the cloud, enabling efficient and selective communication during seismic events. Despite being 20 times smaller than state-of-the-art models and requiring just 186 KB of RAM, SeismicSense achieves exceptional performance, with F1-scores of 99.4% for earthquake detection, 98% for P-wave detection, and 96% for S-wave detection. Additionally, leveraging integer acceleration on modern MCUs enhances efficiency, reducing inference time on Cortex-M MCUs by 18-fold compared to non-accelerated methods, enabling real-time execution.Publicationtype: Conference Paper51 28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Towards solidity smart contract efficiency optimization through code mining(Association for Computing Machinery (ACM), 2025-03); ; Deploying smart contracts and invoking their functions on block-chains incur gas costs, which depend on the operations executed by those functions. This makes optimizing the gas cost of smart contract functions a rewarding goal. However, existing approaches to gas cost optimization of smart contracts mainly involve rule-based optimization or automatic optimization for specific types of patterns. In this paper, we discuss a novel approach to automatically retrieving optimized versions of Solidity functions from a repository of smart contracts. The system identifies and suggests gas-efficient alternatives that maintain functional equivalence by comparing the opcode sequences of individual functions. We evaluate this approach on a dataset of 16,529 functions from real-world contracts, demonstrating substantial gas savings, as high as 34% on average when considering the most similar functions.Publicationtype: Conference Paper30 10