Matthias Weidlich

AAAI Conference 2025 Conference Paper

Privacy-and-Utility-Aware Publishing of Schedules

• Maike Basmer
• Stephan A. Fahrenkrog-Petersen
• Ali Kaan Tutak
• Arik Senderovich
• Matthias Weidlich

Scheduling is adopted in various domains to assign jobs to resources, such that an objective is optimized. While schedules enable the analysis of the underlying system, publishing them also incurs a privacy risk. Recently, privacy attacks on schedules have been proposed, which may reveal sensitive information on the jobs by solving an inverse scheduling problem. In this work, we study the protection against such attacks. We formulate the problem of privacy-and-utility preservation of schedules, which bounds both, the privacy leakage and the loss in the utility of the schedule due to obfuscation. We address the problem based on a set of perturbation functions for schedules, study their instantiations for standard scheduling problems, and implement privacy-and-utility-aware publishing of a schedule using constraint programming. Experiments with synthetic and real-world schedules demonstrate the feasibility, robustness, and effectiveness of our mechanism.

AAAI Conference 2023 Conference Paper

Privacy Attacks on Schedule-Driven Data

• Stephan A. Fahrenkrog-Petersen
• Arik Senderovich
• Alexandra Tichauer
• Ali Kaan Tutak
• J. Christopher Beck
• Matthias Weidlich

Schedules define how resources process jobs in diverse domains, reaching from healthcare to transportation, and, therefore, denote a valuable starting point for analysis of the underlying system. However, publishing a schedule may disclose private information on the considered jobs. In this paper, we provide a first threat model for published schedules, thereby defining a completely new class of data privacy problems. We then propose distance-based measures to assess the privacy loss incurred by a published schedule, and show their theoretical properties for an uninformed adversary, which can be used as a benchmark for informed attacks. We show how an informed attack on a published schedule can be phrased as an inverse scheduling problem. We instantiate this idea by formulating the inverse of a well-studied single-machine scheduling problem, namely minimizing the total weighted completion times. An empirical evaluation for synthetic scheduling problems shows the effectiveness of informed privacy attacks and compares the results to theoretical bounds on uninformed attacks.

AAAI Conference 2019 Conference Paper

Congestion Graphs for Automated Time Predictions

• Arik Senderovich
• J. Christopher Beck
• Avigdor Gal
• Matthias Weidlich

Time prediction is an essential component of decision making in various Artificial Intelligence application areas, including transportation systems, healthcare, and manufacturing. Predictions are required for efficient resource allocation and scheduling, optimized routing, and temporal action planning. In this work, we focus on time prediction in congested systems, where entities share scarce resources. To achieve accurate and explainable time prediction in this setting, features describing system congestion (e. g. , workload and resource availability), must be considered. These features are typically gathered using process knowledge, (i. e. , insights on the interplay of a system’s entities). Such knowledge is expensive to gather and may be completely unavailable. In order to automatically extract such features from data without prior process knowledge, we propose the model of congestion graphs, which are grounded in queueing theory. We show how congestion graphs are mined from raw event data using queueing theory based assumptions on the information contained in these logs. We evaluate our approach on two real-world datasets from healthcare systems where scarce resources prevail: an emergency department and an outpatient cancer clinic. Our experimental results show that using automatic generation of congestion features, we get an up to 23% improvement in terms of relative error in time prediction, compared to common baseline methods. We also detail how congestion graphs can be used to explain delays in the system.

TCS Journal 2018 Journal Article

Interleaving isotactics – An equivalence notion on behaviour abstractions

• Artem Polyvyanyy
• Jan Sürmeli
• Matthias Weidlich

IJCAI Conference 2017 Conference Paper

Retaining Data from Streams of Social Platforms with Minimal Regret

• Nguyen Thanh Tam
• Matthias Weidlich
• Duong Chi Thang
• Hongzhi Yin
• Nguyen Quoc Viet Hung

Today's social platforms, such as Twitter and Facebook, continuously generate massive volumes of data. The resulting data streams exceed any reasonable limit for permanent storage, especially since data is often redundant, overlapping, sparse, and generally of low value. This calls for means to retain solely a small fraction of the data in an online manner. In this paper, we propose techniques to effectively decide which data to retain, such that the induced loss of information, the regret of neglecting certain data, is minimized. These techniques enable not only efficient processing of massive streaming data, but are also adaptive and address the dynamic nature of social media. Experiments on large-scale real-world datasets illustrate the feasibility of our approach in terms of both, runtime and information quality.