Anna Sperotto | Searcharxiv

arXiv2025

From artificial to circular intelligence to support the well-being of our habitat

The proliferation of machine learning and artificial intelligence redefines the interaction between the anthropogenic and natural elements of our habitat.The use of monitoring tools, processing facilities and the internet of things supports the assessment of planetary health at any given time through automation. However, these data, natural resources and infrastructure intensive technologies are not neutral on the Earth. As the community of AI practitioners works on the creation of tools with minimal socio-environmental impacts, we contribute to the these efforts by proposing a novel conceptual and procedural framework which we call Circular Intelligence or CIntel. CIntel leverages a bottom-up and community-driven approach to learn from the ability of nature to regenerate and adapt. CIntel incorporates ethical principles in its technical design to preserve the stability of the habitat, while also increasing the well-being of its inhabitants by design.

cs.CY↗

arXiv2022

Saving Brian's Privacy: the Perils of Privacy Exposure through Reverse DNS

Given the importance of privacy, many Internet protocols are nowadays designed with privacy in mind (e.g., using TLS for confidentiality). Foreseeing all privacy issues at the time of protocol design is, however, challenging and may become near impossible when interaction out of protocol bounds occurs. One demonstrably not well understood interaction occurs when DHCP exchanges are accompanied by automated changes to the global DNS (e.g., to dynamically add hostnames for allocated IP addresses). As we will substantiate, this is a privacy risk: one may be able to infer device presence and network dynamics from virtually anywhere on the Internet -- and even identify and track individuals -- even if other mechanisms to limit tracking by outsiders (e.g., blocking pings) are in place. We present a first of its kind study into this risk. We identify networks that expose client identifiers in reverse DNS records and study the relation between the presence of clients and said records. Our results show a strong link: in 9 out of 10 cases, records linger for at most an hour, for a selection of academic, enterprise and ISP networks alike. We also demonstrate how client patterns and network dynamics can be learned, by tracking devices owned by persons named Brian over time, revealing shifts in work patterns caused by COVID-19 related work-from-home measures, and by determining a good time to stage a heist.

cs.NI↗