TraVaS: Differentially Private Trace Variant Selection for Process Mining

In the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. However, these algorithms face several practical limitations such as high computational complexity, introducing fake variants, removing frequent variants, and a bounded variant length. In this paper, we introduce a new approach for direct differentially private trace variant release which uses anonymized \textit{partition selection} strategies to overcome the aforementioned restraints. Experimental results on real-life event data show that our algorithm outperforms state-of-the-art methods in terms of both plain data utility and result utility preservation

TraVaG: Differentially Private Trace Variant Generation Using GANs

Process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant. State-of-the-art research mainly focuses on providing privacy guarantees, e.g., differential privacy, for trace variants that are used by the main process mining techniques, e.g., process discovery. However, privacy preservation techniques for releasing trace variants still do not fulfill all the requirements of industry-scale usage. Moreover, providing privacy guarantees when there exists a high rate of infrequent trace variants is still a challenge. In this paper, we introduce TraVaG as a new approach for releasing differentially private trace variants based on \text{Generative Adversarial Networks} (GANs) that provides industry-scale benefits and enhances the level of privacy guarantees when there exists a high ratio of infrequent variants. Moreover, TraVaG overcomes shortcomings of conventional privacy preservation techniques such as bounding the length of variants and introducing fake variants. Experimental results on real-life event data show that our approach outperforms state-of-the-art techniques in terms of privacy guarantees, plain data utility preservation, and result utility preservation

First Implementation of a Two-Stage DC-DC Conversion Powering Scheme for the CMS Phase-2 Outer Tracker

The 2S silicon strip modules for the CMS Phase-2 tracker upgrade will require two operating voltages. These will be provided via a two-step DC-DC conversion powering scheme, in which one DC-DC converter delivers 2.5\,V while the second DC-DC converter receives 2.5\,V at its input and converts it to 1.2\,V. The DC-DC converters will be mounted on a flex PCB, the service hybrid, together with an opto-electrical converter module (VTRx+) and a serializer (LP-GBT). The service hybrid will be mounted directly on the 2S module. A prototype service hybrid has been developed and its performance has been evaluated, including radiative and conductive noise emissions, and efficiency. In addition system tests with a prototype module have been performed. In this report the service hybrid will be described and the test results will be summarized