More Like this

1. K9db: Privacy-Compliant Storage For Web Applications By Construction

   Dak_Albab, Kinan; Sharma, Ishan; Adam, Justus; Kilimnik, Benjamin; Jeyaraj, Aaron; Paul, Raj; Agvanian, Artem; Spiegelberg, Leonhard; Schwarzkopf, Malte (July 2023, Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI))

   Data privacy laws like the EU’s GDPR grant users new rights, such as the right to request access to and deletion of their data. Manual compliance with these requests is error-prone and imposes costly burdens especially on smaller organizations, as non-compliance risks steep fines. K9db is a new, MySQL-compatible database that complies with privacy laws by construction. The key idea is to make the data ownership and sharing semantics explicit in the storage system. This requires K9db to capture and enforce applications’ complex data ownership and sharing semantics, but in exchange simplifies privacy compliance. Using a small set of schema annotations, K9db infers storage organization, generates procedures for data retrieval and deletion, and reports compliance errors if an application risks violating the GDPR. Our K9db prototype successfully expresses the data sharing semantics of real web applications, and guides developers to getting privacy compliance right. K9db also matches or exceeds the performance of existing storage systems, at the cost of a modest increase in state size. 

   more » « less
2. Linking Personally Identifiable Information from the Dark Web to the Surface Web: A Deep Entity Resolution Approach

   https://doi.org/10.1109/ICDMW51313.2020.00072  

   Lin, Fangyu; Liu, Yizhi; Ebrahimi, Mohammadreza; Ahmad-Post, Zara; Hu, James Lee; Xin, Jingyu; Samtani, Sagar; Li, Weifeng; Chen, Hsinchun (November 2020, International Conference on Data Mining Workshops (ICDMW))

   null (Ed.)

   The information privacy of the Internet users has become a major societal concern. The rapid growth of online services increases the risk of unauthorized access to Personally Identifiable Information (PII) of at-risk populations, who are unaware of their PII exposure. To proactively identify online at-risk populations and increase their privacy awareness, it is crucial to conduct a holistic privacy risk assessment across the internet. Current privacy risk assessment studies are limited to a single platform within either the surface web or the dark web. A comprehensive privacy risk assessment requires matching exposed PII on heterogeneous online platforms across the surface web and the dark web. However, due to the incompleteness and inaccuracy of PII records in each platform, linking the exposed PII to users is a non-trivial task. While Entity Resolution (ER) techniques can be used to facilitate this task, they often require ad-hoc, manual rule development and feature engineering. Recently, Deep Learning (DL)-based ER has outperformed manual entity matching rules by automatically extracting prominent features from incomplete or inaccurate records. In this study, we enhance the existing privacy risk assessment with a DL-based ER method, namely Multi-Context Attention (MCA), to comprehensively evaluate individuals’ PII exposure across the different online platforms in the dark web and surface web. Evaluation against benchmark ER models indicates the efficacy of MCA. Using MCA on a random sample of data breach victims in the dark web, we are able to identify 4.3% of the victims on the surface web platforms and calculate their privacy risk scores. 

   more » « less
3. Sesame: Practical End-to-End Privacy Compliance with Policy Containers and Privacy Regions

   https://doi.org/10.1145/3694715.3695984  

   Dak_Albab, Kinan; Agvanian, Artem; Aby, Allen; Tiffany, Corinn; Portland, Alexander; Ridley, Sarah; Schwarzkopf, Malte (November 2024, ACM)

   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
4. Automated Analysis of Changes in Privacy Policies: A Structured Self-Attentive Sentence Embedding Approach

   https://doi.org/10.25300/misq/2024/17115  

   Lin, Fangyu; Samtani, Sagar; Zhu, Hongyi; Brandimarte, Laura; Chen, Hsinchun (December 2024, MIS Quarterly)

   The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as the General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a self-attentive annotation system (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art machine learning (ML) and deep learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research. 

   more » « less
5. The Privacy Policy Landscape After the GDPR

   https://doi.org/10.2478/popets-2020-0004  

   Linden, Thomas; Khandelwal, Rishabh; Harkous, Hamza; Fawaz, Kassem (January 2020, Proceedings on Privacy Enhancing Technologies)

   Abstract The EU General Data Protection Regulation (GDPR) is one of the most demanding and comprehensive privacy regulations of all time. A year after it went into effect, we study its impact on the landscape of privacy policies online. We conduct the first longitudinal, in-depth, and at-scale assessment of privacy policies before and after the GDPR. We gauge the complete consumption cycle of these policies, from the first user impressions until the compliance assessment. We create a diverse corpus of two sets of 6,278 unique English-language privacy policies from inside and outside the EU, covering their pre-GDPR and the post-GDPR versions. The results of our tests and analyses suggest that the GDPR has been a catalyst for a major overhaul of the privacy policies inside and outside the EU. This overhaul of the policies, manifesting in extensive textual changes, especially for the EU-based websites, comes at mixed benefits to the users. While the privacy policies have become considerably longer, our user study with 470 participants on Amazon MTurk indicates a significant improvement in the visual representation of privacy policies from the users’ perspective for the EU websites. We further develop a new workflow for the automated assessment of requirements in privacy policies. Using this workflow, we show that privacy policies cover more data practices and are more consistent with seven compliance requirements post the GDPR. We also assess how transparent the organizations are with their privacy practices by performing specificity analysis. In this analysis, we find evidence for positive changes triggered by the GDPR, with the specificity level improving on average. Still, we find the landscape of privacy policies to be in a transitional phase; many policies still do not meet several key GDPR requirements or their improved coverage comes with reduced specificity. 

   more » « less