Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, even with simple rules, such as “IF the entrance Netatmo Wheather Station detects a temperature above 30 \({}^{\circ}C\) ( \(86^{\circ}F\) ) THEN open the shutters in the living room,” might expose users to potential security and privacy risks (e.g., the execution of the previous rule might provide an easy entry point for thieves, especially during the summer vacation period). The goal of our research is to make the users capable of understanding and mitigating the threats and risks associated with the execution of IF-THEN rules. To this end, we define a new challenging task, namely generating post hoc justifications of privacy and security risks associated with automation rules, and propose a novel natural language generation strategy based on hybrid prompt learning producing justifications in the form of real-life threat scenarios. The proposed strategy allows for prompt customization with task-specific information, providing contextual details enabling to grasp the nuances and subtleties of the domain language, resulting in more coherent justifications. The experiments conducted on the if-this-then-that (IFTTT) platform show that our method produces effective justifications, improving the explainability of discrete and hybrid prompting methods up to 27% in BLEURT score. The code of the software is publicly available on GitHub 1 .

Over the past decade, there have been numerous extensions to the definition of Functional Dependency (fd), culminating in the introduction of Relaxed Functional Dependency (rfd), offering more flexible constraints compared to traditionalfds. This increased flexibility makesrfds well-suited for exploring and profiling data in datasets with lower data quality. However, efficiently identifyingrfds within dynamic data sources presents a significant challenge, as it requires processing an entire dataset from scratch whenever modifications occur. To tackle this problem, incremental discovery algorithms have been defined, but they often suffer when the frequency and the size of batches of updates increase. This article presents a new algorithm, namelyD-IndiBits, relying on a new decentralized architecture to balance the workload that drives the incremental discovery process ofIndiBits, which is based on bitwise operators for computing attribute similarities. Experiments demonstrateD-IndiBits's effectiveness compared tofdandrfddiscovery algorithms on both static and dynamic real-world data. With batches of modifications of sizes 10 k and 100 k,D-IndiBitsis capable of updating the set ofrfds in a few seconds, whereas all other approaches often employ more than 3 hours.

One of the main challenges in data profiling is to efficiently extract metadata from dynamic information sources, by avoiding the processing of the whole dataset from scratch upon modifications. In this paper, we present IndiBits, an algorithm for discovering relaxed functional dependencies (RFDs for short), which represent data relationships relying on approximate matching paradigms. IndiBits is able to dynamically infer and update the RFDs holding on a dataset upon modification operations performed on it. It exploits a binary representation of data similarities, a new validation method, and specific search methods, to dynamically update the set of RFDs, based on previously holding RFDs and the type of modifications performed over data. Experimental results demonstrate the effectiveness of IndiBits on real-world datasets, even in comparison with FD and RFD discovery algorithms in both static and dynamic scenarios.