From Anonymous to Identified: Preventing Voluntary Data Disclosure in Onion Services
A client-side NLP extension for Tor Browser detects onion-service form fields that solicit identifying data, achieving over 90% precision, accuracy, recall, and F1-score with negligible overhead.- doi
- 10.1145/3720533.3750066
- isbn
- 979-8-4007-1533-4
- name
- From Anonymous to Identified: Preventing Voluntary Data Disclosure in Onion Services
- pages
- 5–9
- source
- publisher-bits-xml
- acm_url
- https://dl.acm.org/doi/10.1145/3720533.3750066
- authors
- Vincenzo De Angelis, Sara Lazzaro, Francesco Buccafurri
- doi_url
- https://doi.org/10.1145/3720533.3750066
- license
- © 2025 Copyright held by the owner/author(s).
- summary
- A client-side NLP extension for Tor Browser detects onion-service form fields that solicit identifying data, achieving over 90% precision, accuracy, recall, and F1-score with negligible overhead.
- keywords
- Tor browser, NLP, personal data, de-anonymization
- published
- 2025-09-15
- conference
- HT '25 Adjunct: Adjunct Proceedings of the 36th ACM Conference on Hypertext and Social Media, Chicago, IL, USA
- open_access
- false
- acm_html_url
- https://dl.acm.org/doi/full/10.1145/3720533.3750066
- ccs_concepts
- Security and privacy~Privacy protections; Security and privacy~Usability in security and privacy; Security and privacy~Pseudonymity, anonymity and untraceability
- displayAuthor
- Vincenzo De Angelis, Sara Lazzaro, Francesco Buccafurri
- proceedings_url
- https://dl.acm.org/doi/proceedings/10.1145/3720533
- displayPublishTime
- 2025-09-15
- acm_reference_format
- (empty)
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