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Privacy2Practice: Leveraging Automated Analysis for Privacy Policy Transparency and Compliance [Privacy2Practice: Leveraging Automated Analysis for Privacy Policy Transparency and Compliance]
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Privacy policies disclose how an organization collects and handles personal information. Recent work has made progress in leveraging natural language processing (NLP) to automate privacy policy analysis and extract data collection statements from different sentences, considered in isolation from each other. In this paper, we view and analyze, for the first time, the entire text of a privacy policy in an integrated way. In terms of methodology: (1) we define PoliGraph , a type of knowledge graph that captures statements in a privacy policy as relations between different parts of the text; and (2) we develop an NLP-based tool, PoliGraph-er , to automatically extract PoliGraph from the text. In addition, (3) we revisit the notion of ontologies, previously defined in heuristic ways, to capture subsumption relations between terms. We make a clear distinction between local and global ontologies to capture the context of individual privacy policies, application domains, and privacy laws. Using a public dataset for evaluation, we show that PoliGraph-er identifies 40% more collection statements than prior state-of-the-art, with 97% precision. In terms of applications, PoliGraph enables automated analysis of a corpus of privacy policies and allows us to: (1) reveal common patterns in the texts across different privacy policies, and (2) assess the correctness of the terms as defined within a privacy policy. We also apply PoliGraph to: (3) detect contradictions in a privacy policy, where we show false alarms by prior work, and (4) analyze the consistency of privacy policies and network traffic, where we identify significantly more clear disclosures than prior work.more » « less
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An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users' data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.more » « less
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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).more » « less
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In recent years, well-known cyber breaches have placed growing pressure on organizations to implement proper privacy and data protection standards. Attacks involving the theft of employee and customer personal information have damaged the reputations of well-known brands, resulting in significant financial costs. As a result, governments across the globe are actively examining and strengthening laws to better protect the personal data of its citizens. The General Data Protection Regulation (GDPR) updates European privacy law with an array of provisions that better protect consumers and require organizations to focus on accounting for privacy in their business processes through “privacy-by-design” and “privacy by default” principles. In the US, the National Privacy Research Strategy (NPRS), makes several recommendations that reinforce the need for organizations to better protect data. In response to these rapid developments in privacy compliance, data flow mapping has emerged as a valuable tool. Data flow mapping depicts the flow of data through a system or process, enumerating specific data elements handled, while identifying the risks at different stages of the data lifecycle. This Article explains the critical features of a data flow map and discusses how mapping may improve the transparency of the data lifecycle, while recognizing the limitations in building out data flow maps and the difficulties of maintaining updated maps. The Article then explores how data flow mapping may support data collection, transfer, storage, and destruction practices pursuant to various privacy regulations. Finally, a hypothetical case study is presented to show how data flow mapping was used by an organization to stay compliant with privacy rules and to improve the transparency of information flowsmore » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.5220/0013518600003979
