Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Siby, Sandra"'
Personalized learning is a proposed approach to address the problem of data heterogeneity in collaborative machine learning. In a decentralized setting, the two main challenges of personalization are client clustering and data privacy. In this paper,
Externí odkaz:
http://arxiv.org/abs/2405.17697
Privacy-enhancing blocking tools based on filter-list rules tend to break legitimate functionality. Filter-list maintainers could benefit from automated breakage detection tools that allow them to proactively fix problematic rules before deploying th
Externí odkaz:
http://arxiv.org/abs/2405.05196
Machine-learning (ML) models are increasingly being deployed on edge devices to provide a variety of services. However, their deployment is accompanied by challenges in model privacy and auditability. Model providers want to ensure that (i) their pro
Externí odkaz:
http://arxiv.org/abs/2404.00190
While privacy-focused browsers have taken steps to block third-party cookies and mitigate browser fingerprinting, novel tracking techniques that can bypass existing countermeasures continue to emerge. Since trackers need to share information from the
Externí odkaz:
http://arxiv.org/abs/2308.03417
Autor:
Munir, Shaoor, Siby, Sandra, Iqbal, Umar, Englehardt, Steven, Shafiq, Zubair, Troncoso, Carmela
As third-party cookie blocking is becoming the norm in browsers, advertisers and trackers have started to use first-party cookies for tracking. We conduct a differential measurement study on 10K websites with third-party cookies allowed and blocked.
Externí odkaz:
http://arxiv.org/abs/2208.12370
Autor:
Siby, Sandra, Barman, Ludovic, Wood, Christopher, Fayed, Marwan, Sullivan, Nick, Troncoso, Carmela
Website fingerprinting (WF) is a well-know threat to users' web privacy. New internet standards, such as QUIC, include padding to support defenses against WF. Previous work only analyzes the effectiveness of defenses when users are behind a VPN. Yet,
Externí odkaz:
http://arxiv.org/abs/2203.07806
Millions of web users directly depend on ad and tracker blocking tools to protect their privacy. However, existing ad and tracker blockers fall short because of their reliance on trivially susceptible advertising and tracking content. In this paper,
Externí odkaz:
http://arxiv.org/abs/2107.11309
Virtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations
Externí odkaz:
http://arxiv.org/abs/1906.09682
In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As
Externí odkaz:
http://arxiv.org/abs/1701.05007
Autor:
Siby, Sandra Deepthy
Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f18879c7673b846ef4a1809d40553f7f