Matrix Profile (MP) is a data mining structure increasingly used for time series analysis in both academic and industrial contexts. Given its application to sensitive domains such as healthcare or energy monitoring, it is crucial to examine associated privacy risks, especially since MPs are often shared or processed in untrusted environments like the cloud. While recent studies suggest that MPs offer some privacy protection, this assumption remains largely untested. This paper analyzes the privacy risks of MP publication through the lens of EU data protection law, focusing on singling-out, linkability, and inference risks. We introduce a reconstruction technique based on constraint optimization, capable of recovering approximate original time series from their MPs, leading to severe privacy attacks. Experiments on real-world datasets reveal vulnerabilities to all attack types, with reconstructed series reaching up to 0.99 Pearson Correlation with the original.

Local Differential Privacy (LDP) provides strong, formal privacy guarantees without requiring a trusted curator, making it a promising approach for privacy-preserving data collection and analysis. However, despite extensive research, practitioners may struggle to understand how to tune LDP parameters and anticipate the impact on data utility and attack risks for their specific scenarios. To address this gap, we demonstrate LDP-Toolbox, the first interactive, web-based toolbox (implemented in Python) that enables practical, analytical visualization of trade-offs between privacy loss (ε), utility loss, and vulnerability to attacks. The toolbox supports exploration of these trade-offs using real-world datasets from different domains; in this demonstration, we focus on discrete personal attributes and location-based scenarios. By providing intuitive, visual insights, LDP-Toolbox lowers the barrier to deploying LDP in real applications and helps bridge the gap between theoretical guarantees and practical adoption. The toolbox is open-source on PyPI (https://pypi.org/project/ldp-toolbox) and a video is available on our GitHub repository (https://github.com/hharcolezi/ldp-toolbox).

Teleworking has become a social gain following the COVID-19 lock-downs. In many professions, remote work is becoming a common practice, either at the employee's home or in a shared space nearby. However, this creates an implicit private/work-life tension as private activities may be carried out during work time and vice versa. Detecting boundary crossings is of outmost relevance - they serve as evidence of the workers' breaks and right to rest. However, this must be achieved without excessive surveillance. Existing activity recognition techniques either do not address the border crossing problem or require a priori training. To address this issue, this article proposes TELESAFE , a boundary crossing detector solution for teleworking. TELESAFE does not require any training nor instrumentation of the teleworker home and can be run locally in resource-constrained devices. To illustrate its suitability, it is applied on electric consumption trails so as to enable self and third-party assessment (e.g., work inspectors) on working conditions. Results on real-world datasets show a Fscore over 90% for identifying private activities involving one or more devices with usage patterns of varying lengths. Interestingly, TELESAFE outperforms Machine and Deep-Learning approaches in the most complex settings, without the burden of training.