Majid Lotfian Delouee received Master's and Bachelor's degrees in Software Engineering with majors in Networks and Information Systems, especially for IoT applications. Currently, he is doing research on providing a trade-off between quality monitoring and privacy protection in event processing systems. In addition, he collaborates with the Parrot Project, which is a joint research project involving the University of Oslo (Norway), the Technical University of Darmstadt (Germany), and the University of Groningen (The Netherlands).
' ' Majid is a member of Parrot project which is an international collaboration between the University of Oslo (Norway), the Technical University of Darmstadt (Germany), and the University of Groningen (The Netherlands) funded by the Research Council of Norway (2020 – 2023).' '
Guaranteeing precise perception for fully autonomous driving in diverse driving conditions requires continuous improvement and training. In vehicular networks, federated learning (FL) facilitates this by enabling model training without sharing raw sensory data. As an extension, clustered FL reduces communication overhead and aligns well with the dynamic nature of these networks. However, current literature on this topic does not consider critical dimensions of FL, including (1) the correlation between perception performance and the networking overhead, (2) the limited vehicle storage, (3) the need for training with freshly captured data, and (4) the impact of non-IID data and varying traffic densities. To fill these research gaps, we introduce AR-CFL, an Adaptive Resource-aware Clustered Federated Learning framework. AR-CFL utilizes clustered FL to collectively model the environment of connected vehicles, integrating models from all vehicles and ensuring universal accessibility to the refined model. AR-CFL dynamically enhances system efficiency by adaptively adjusting the number of clusters and specific in-cluster participant selection strategies. Using AR-CFL, we systematically study the scenario of online car detection model training on non-IID data across varied conditions. The evaluation results highlight the robust detection performance exhibited by the trained model employing the clustered FL approach, despite the constraints posed by limited vehicle storage capacity. Furthermore, our investigation unveils superior training performance with clustered FL in comparison to specific classical FL scenarios, increasing the training efficiency in terms of participating nodes by up to 25% and reducing cellular communication by 33%.
Although privacy-preserving mechanisms endeavor to safeguard sensitive information at the attribute level, detected event patterns can still disclose privacy-sensitive knowledge in distributed complex event processing systems (DCEP). Events might not be inherently sensitive, but their aggregation into a pattern could still breach privacy. In this paper, we study in the context of APP-CEP the problem of integrating pattern-level privacy in event-based systems by selective assignment of obfuscation techniques to conceal private information. Compared to state-of-the-art techniques, we seek to enforce privacy independent of the actual events in streams. To support this, we acquire queries and privacy requirements using CEP-like patterns. The protection of privacy is accomplished through generating pattern dependency graphs, leading to dynamically appointing those techniques that have no consequences on detecting other sensitive patterns, as well as non-sensitive patterns required to provide acceptable Quality of Service. Besides, we model the knowledge that might be possessed by potential adversaries to violate privacy and its impacts on the obfuscation procedure. We assessed the performance of APP-CEP in a real-world scenario involving an online retailer’s transactions. Our evaluation results demonstrate that APP-CEP successfully provides a privacy-utility trade-off. Modeling the background knowledge also effectively prevents adversaries from realizing the modifications in the input streams.
In event processing systems, detected event patterns can reveal privacy-sensitive information. In this paper, we propose and discuss how to integrate pattern-level privacy protection in event-based systems. Compared to state-of-the-art approaches, we aim to enforce privacy independent of the particularities of specific operators. We accomplish this by supporting the flexible integration of multiple obfuscation techniques and studying deployment strategies for privacy-enforcing mechanisms. Moreover, we share ideas on how to model the adversary’s knowledge to better select appropriate obfuscation techniques for the discussed deployment strategies. Initial results indicate that flexibly choosing obfuscation techniques and deployment strategies is essential to conceal privacy-sensitive event patterns accurately.
Sensory data profoundly influences the quality of detected events in a distributed complex event processing system (DCEP). Since each sensor’s status is unstable at runtime, a single sensing assignment is often insufficient to fulfill the consumer’s quality requirements. In this paper, we study in the context of AQuA-CEP the problem of dynamic quality monitoring and adaptation of complex event processing by active integration of suitable data sources. To support this, in AQuA-CEP, queries to detect complex events are supplemented with consumer-definable quality policies that are evaluated and used to autonomously select (or even configure) suitable data sources of the sensing infrastructure. In addition, we studied different forms of expressing quality policies and analyzed how it affects the quality monitoring process. Various modes of evaluating and applying quality-related adaptations and their impacts on correlation efficiency are addressed, too. We assessed the performance of AQuA-CEP in IoT scenarios by utilizing the notion of the quality policy alongside the query processing adaptation using knowledge derived from quality monitoring. The results show that AQuA-CEP can improve the performance of DCEP systems in terms of the quality of results while fulfilling the consumer’s quality requirements. Quality-based adaptation can also increase the network’s lifetime by optimizing the sensor’s energy consumption due to efficient data source selection.
This paper investigates how to complement Complex Event Processing (CEP) with dynamic quality monitoring mechanisms and support the dynamic integration of suitable sensory data sources. In the proposed approach, queries to detect complex events are annotated with consumer-definable quality policies that are evaluated and used to autonomously assign (or even configure) suitable data sources of the sensing infrastructure. We present and study different forms of expressing quality policies and explore how they affect the process of quality monitoring including different modes of assessing and applying quality-related adaptations. A performance study in an IoT scenario shows that the proposed mechanisms in supporting quality policy monitoring and adaptively selecting suitable data sources succeed in enhancing the acquired quality of results while fulfilling consumers' quality requirements. We show that the quality-based selection of sensor sources also extends the network's lifetime by optimizing the data sources' energy consumption.
