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Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been used to create experimental tools to interpret privacy policies, but there has been a lack of large privacy policy corpora to facilitate the creation of large-scale semi-supervised and unsupervised models to interpret and simplify privacy policies. Thus, we present the PrivaSeer Corpus of 1,005,380 English language website privacy policies collected from the web. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies, and it surpasses the aggregate of unique websites represented in all other publicly available privacy policy corpora combined. We describe a corpus creation pipeline with stages that include a web crawler, language detection, document classification, duplicate and near-duplicate removal, and content extraction. We employ an unsupervised topic modelling approach to investigate the contents of policy documents in the corpus and discuss the distribution of topics in privacy policies at web scale. We further investigate the relationship between privacy policy domain PageRanks and text features of the privacy policies. Finally, we use the corpus to pretrain PrivBERT, a transformer-based privacy policy language model, and obtain state of the art results on the data practice classification and question answering tasks.
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This content will become publicly available on November 4, 2025
Sesame: Practical End-to-End Privacy Compliance with Policy Containers and Privacy Regions
Web applications are governed by privacy policies, but developers lack practical abstractions to ensure that their code actually abides by these policies. This leads to frequent oversights, bugs, and costly privacy violations. Sesame is a practical framework for end-to-end privacy policy enforcement. Sesame wraps data in policy containers that associate data with policies that govern its use. Policy containers force developers to use privacy regions when operating on the data, and Sesame combines sandboxing and a novel static analysis to prevent privacy regions from leaking data. Sesame enforces a policy check before externalizing data, and it supports custom I/O via reviewed, signed code. Experience with four web applications shows that Sesame’s automated guarantees cover 95% of application code, with the remaining 5% needing manual review. Sesame achieves this with reasonable application developer effort and imposes 3–10% performance overhead (10–55% with sandboxes).
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- Award ID(s):
- 2045170
- PAR ID:
- 10579612
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400712517
- Page Range / eLocation ID:
- 709 to 725
- Format(s):
- Medium: X
- Location:
- Austin, TX, USA
- Sponsoring Org:
- National Science Foundation
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