arXiv subjects

Tina Marjanov

Publications and source records attributed to Tina Marjanov.

2 recordsLinked to original sources

arXiv2026

A Large-Scale Study of Telegram Bots

Telegram, initially a messaging app, has evolved into a platform where users can interact with various services through programmable applications, bots. Bots provide a wide range of uses, from moderating groups, helping with online shopping, to even executing trades in financial markets. However, Telegram has been increasingly associated with various illicit activities -- financial scams, stolen data, non-consensual image sharing, among others, raising concerns bots may be facilitating these operations. This paper is the first to characterize Telegram bots at scale, through the following contributions. First, we offer the largest general-purpose message dataset and the first bot dataset. Through snowball sampling from two published datasets, we uncover over 67,000 additional channels, 492 million messages, and 32,000 bots. Second, we develop a system to automatically interact with bots in order to extract their functionality. Third, based on their description, chat responses, and the associated channels, we classify bots into several domains. Fourth, we investigate the communities each bot serves, by analyzing supported languages, usage patterns (e.g., duration, reuse), and network topology. While our analysis discovers useful applications such as crowdsourcing, we also identify malicious bots (e.g., used for financial scams, illicit underground services) serving as payment gateways, referral systems, and malicious AI endpoints. By exhorting the research community to look at bots as software infrastructure, this work hopes to foster further research useful to content moderators, and to help interventions against illicit activities.

cs.CR↗

arXiv2024

A Confidential Computing Transparency Framework for a Comprehensive Trust Chain

Confidential Computing enhances privacy of data in-use through hardware-based Trusted Execution Environments (TEEs) that use attestation to verify their integrity, authenticity, and certain runtime properties, along with those of the binaries they execute. However, TEEs require user trust, as attestation alone cannot guarantee the absence of vulnerabilities or backdoors. Enhanced transparency can mitigate the reliance on naive trust. Some organisations currently employ various transparency measures, including open-source firmware, publishing technical documentation, or undergoing external audits, but these require investments with unclear returns. This may discourage the adoption of transparency, leaving users with limited visibility into system privacy measures. Additionally, the lack of standardisation complicates meaningful comparisons between implementations. To address these challenges, we propose a three-level conceptual framework providing organisations with a practical pathway to incrementally improve Confidential Computing transparency. To evaluate whether our transparency framework contributes to an increase in end-user trust, we conducted an empirical study with over 800 non-expert participants. The results indicate that greater transparency improves user comfort, with participants willing to share various types of personal data across different levels of transparency. The study also reveals misconceptions about transparency, highlighting the need for clear communication and user education.

cs.CR↗