Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies

Srinath, Mukund; Wilson, Shomir; Giles, C. Lee

doi:10.48550/arxiv.2004.11131

Cited by 3 publications

(7 citation statements)

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“…The taxonomy covers privacy practices mentioned in the privacy policies of the websites. In the past few years, several works have trained classifiers using the taxonomy for automated policy analysis [22,36,43,45,47]. Researchers have also used automated policy analysis to check for consistency within the policy [5,6], as well as consistency with the code [51,52].…”

Section: Google Data Safety Sectionmentioning

confidence: 99%

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The Overview of Privacy Labels and their Compatibility with Privacy Policies

Khandelwal¹,

Nayak²,

Chung³

et al. 2023

Preprint

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Section: Google Data Safety Sectionmentioning

confidence: 99%

“…Following prior works [43,45], we use DistilBERT [40] to train the classification models. We then use these models to extract privacy practices from policies.…”

Section: Privacy Policy Analysismentioning

confidence: 99%

The Overview of Privacy Labels and their Compatibility with Privacy Policies

Khandelwal¹,

Nayak²,

Chung³

et al. 2023

Preprint

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“…The privacy policies for the PrivaSeer search engine come from the PrivaSeer Corpus 5 [21,22]. Srinath et al built the PrivaSeer Corpus using two separate crawls of the web.…”

Section: Data Collectionmentioning

confidence: 99%

“…The PrivaSeer Corpus was created by training a random forest model to classify whether a document is a privacy policy. Srinath et al [21] labeled 1000 crawled documents as either a privacy policy or not. We used the labeled data to train a machine learning model and obtained the probability of a document being a privacy policy.…”

Section: Rankingmentioning

confidence: 99%

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PrivaSeer: A Privacy Policy Search Engine

Srinath

Sundareswara

Giles

et al. 2021

Lecture Notes in Computer Science

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Web privacy policies are used by organisations to disclose their privacy practices to users on the web. However, users often do not read privacy policies because they are too long, time consuming, or too complicated. Attempts to simplify privacy policies using natural language processing have achieved some success, but they face limitations of scalability and generalization. While this puts an onus on researchers and policy regulators to protect users against unfair privacy practices, they often lack a large-scale collection of policies to study the state of internet privacy. To remedy this bottleneck, we present PrivaSeer, the first privacy policy search engine. PrivaSeer has been indexed on 1,400,318 English language website privacy policies and can be used to search privacy policies based on text queries and several search facets. Results can be ranked by PageRank, query-based document relevance, and the probability that a document is a privacy policy. Results also can be filtered by readability, vagueness, industry, and mentions of tracking technology, self-regulatory bodies, or regulations and cross-border agreements in the policy text. PrivaSeer allows legal experts, researchers, and policy regulators to discover privacy trends and policy anomalies in privacy policies at scale. In this paper we present the search interface, ranking technique, and filtering techniques for PrivaSeer. We create two indexes of privacy policies: one including supplementary non-policy content present in privacy policy web pages and one without. We evaluate the functionality of PrivaSeer by comparing ranking techniques on these two indexes.

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Security screening metrics for information‐sharing partnerships

Yu,

Collier,

Thekdi

2024

Risk Analysis

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Recent history has shown both the benefits and risks of information sharing among firms. Information is shared to facilitate mutual business objectives. However, information sharing can also introduce security‐related concerns that could expose the firm to a breach of privacy, with significant economic, reputational, and safety implications. It is imperative for organizations to leverage available information to evaluate security related to information sharing when evaluating current and potential information‐sharing partnerships. The “fine print” or privacy policies of firms can provide a signal of security across a wide variety of firms being considered for new and continued information‐sharing partnerships. In this article, we develop a methodology to gauge and benchmark information security policies in the partner‐selection process that can help direct risk‐based investments in information sharing security. We develop a methodology to collect and interpret firm privacy policies, evaluate characteristics of those policies by leveraging natural language processing metrics and developing benchmarking metrics, and understand how those characteristics relate to one another in information‐sharing partnership situations. We demonstrate the methodology on 500 high‐revenue firms. The methodology and managerial insights will be of interest to risk managers, information security professionals, and individuals forming information sharing agreements across industries.

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Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies

Cited by 3 publications

References 0 publications

The Overview of Privacy Labels and their Compatibility with Privacy Policies

The Overview of Privacy Labels and their Compatibility with Privacy Policies

PrivaSeer: A Privacy Policy Search Engine

Security screening metrics for information‐sharing partnerships

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