Current service-oriented systems rely on asynchronous and recursive service calls. Still when analyzing the behavior of software-oriented systems the calling context of a service call is rarely considered. In earlier work, we could show that common workflow net-based analyses fail to detect deadlocks in a service-oriented system, if they are caused by recursive service calls. To investigate this issue, this work proposes different kinds of service compositions based on the context-sensitivity of recursive service calls and the concept of calling depth. Within this framework, we highlight that context-insensitive deadlock analyses fail, as they ignore the calling context. However, we could show that a context-sensitive deadlock analysis only needs to consider a calling depth of two to be successful. Our key insight is, that context-sensitivity of service composition might impact the correctness of a behavioral analysis.
Augmented Reality (AR) experiences combining real-world and virtual objects have been applied in several areas, such as entertainment, manufacturing, and training. Such experiences commonly use devices that collect data about users and their surrounding environment, which may be shared and disseminated through the Internet. This leads to increased concerns about maintaining the privacy and confidentiality of individuals and companies. However, identifying and mitigating the risks of data breaches in AR applications is an open challenge. In this paper, we present SafeAR, a project aimed at creating automatic tools to ensure the privacy and confidentiality of sensitive data while maintaining seamless, real-time, and persistent physical-digital AR experiences. In SafeAR, we apply machine learning to automatically identify the occurrences of privacy risks in raw data captured by AR applications. We study different risk identification and data sanitization methods and architectural solutions for an ecosystem that processes the raw data captured by AR applications and makes sanitized data available, considering the usual specific requirements (e.g., response time) and resource limitations of AR applications. The proposals are validated on a location-based AR game and a headset application for training and manufacturing.
A novel approach pyStorageLess introduces storage interoperability for serverless functions in federated serverless infrastructures. The serverless functions are deployed only once, and the storage can be dynamically linked to the functions at runtime by the user through control data inputs while invoking the serverless functions. pyStorageLess uses the Von Neumann approach to abstract the function to have computing resources, memory, storage, and input/output data, regardless of the provider that hosts each part. pyStorageLess splits data inputs into value and control, allowing users to dynamically attach the storage at runtime. With such extreme flexibility, users may distribute parts of the function across different providers to improve performance.
Data sharing has become a key factor in data analytics, as it allows organizations to operate on others' data for secondary usages. At the same time, when sharing datasets, it is essential to transform the data to protect sensitive information while still providing data consumers with high-quality access. This balance between privacy and visibility highlights the importance of quantifying privacy to ensure effective data sharing. Entropy, a key measure in information theory, is a natural fit for addressing the challenges of privacy quantification. However, current methods for measuring privacy, particularly those involving temporal and subjective data, face limitations in adaptability and scope. To address these issues, this paper proposes a Quantitative Privacy Evaluation Method Based on Tsallis Entropy for Trustworthy Data Sharing. This approach unifies existing entropy-based privacy models under a single Tsallis entropy framework, improving generalization across various datasets through an adjustable parameter. Building on this model, the paper introduces a method for quantifying the strength of privacy protection, providing theoretical support for assessing the degree of data sharing. Finally, the effectiveness of the proposed Tsallis entropy model in quantifying privacy is analyzed in the context of a multi-center clinical trial scenario.
Serverless computing breaks down applications into workflows of stateless functions, where the output of each function serves as the input for the next. When these workflows are distributed across multiple processing nodes, managing the interactions between nodes is crucial to maintaining overall system performance. However, existing analytical performance models do not cope well with the dependencies involved in distributed workflows when the offloaded jobs arrive in batches. In this paper, we study the problem for two processing resources in tandem and develop a scalable surrogate modeling approach based on neural networks that can be used for serverless resource management purposes. We validate our performance model with both synthetic and real-world AI application traces, demonstrating that our surrogate model achieves a mean average percentage error of about 5%.
