An official website of the United States government
Privacy policies, despite the important information they provide about the collection and use of one's data, tend to be skipped over by most Internet users. In this paper, we seek to make privacy policies more accessible by automatically classifying web privacy. We use natural language processing techniques and multiple machine learning models to determine the effectiveness of each method in the classification method. We also explore the effectiveness of these methods to classify privacy policies of Internet of Things (IoT) devices.
more »
« less
Assessing Purpose-Extraction for Automated Corpora Annotations
Privacy policies contain important information regarding the collection and use of user’s data. As Internet of Things (IoT) devices have become popular during the last years, these policies have become important to protect IoT users from unwanted use of private data collected through them. However, IoT policies tend to be long thus discouraging users to read them. In this paper, we seek to create an automated and annotated corpus for IoT privacy policies through the use of natural language processing techniques. Our method extracts the purpose from privacy policies and allows users to quickly find the important information relevant to their data collection/use.
more »
« less
- Award ID(s):
- 1950416
- PAR ID:
- 10387038
- Date Published:
- Journal Name:
- Proceedings of 2022 REUNS Workshop
- Page Range / eLocation ID:
- 718 to 719
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Privacy labels---standardized, compact representations of data collection and data use practices---are often presented as a solution to the shortcomings of privacy policies. Apple introduced mandatory privacy labels for apps in its App Store in December 2020; Google introduced mandatory labels for Android apps in July 2022. iOS app privacy labels have been evaluated and critiqued in prior work. In this work, we evaluated Android Data Safety Labels and explored how differences between the two label designs impact user comprehension and label utility. We conducted a between-subjects, semi-structured interview study with 12 Android users and 12 iOS users. While some users found Android Data Safety Labels informative and helpful, other users found them too vague. Compared to iOS App Privacy Labels, Android users found the distinction between data collection groups more intuitive and found explicit inclusion of omitted data collection groups more salient. However, some users expressed skepticism regarding elided information about collected data type categories. Most users missed critical information due to not expanding the accordion interface, and they were surprised by collection practices excluded from Android's definitions. Our findings also revealed that Android users generally appreciated information about security practices included in the labels, and iOS users wanted that information added.more » « less
-
Furnell, Steven (Ed.)A huge amount of personal and sensitive data is shared on Facebook, which makes it a prime target for attackers. Adversaries can exploit third-party applications connected to a user’s Facebook profile (i.e., Facebook apps) to gain access to this personal information. Users’ lack of knowledge and the varying privacy policies of these apps make them further vulnerable to information leakage. However, little has been done to identify mismatches between users’ perceptions and the privacy policies of Facebook apps. We address this challenge in our work. We conducted a lab study with 31 participants, where we received data on how they share information in Facebook, their Facebook-related security and privacy practices, and their perceptions on the privacy aspects of 65 frequently-used Facebook apps in terms of data collection, sharing, and deletion. We then compared participants’ perceptions with the privacy policy of each reported app. Participants also reported their expectations about the types of information that should not be collected or shared by any Facebook app. Our analysis reveals significant mismatches between users’ privacy perceptions and reality (i.e., privacy policies of Facebook apps), where we identified over-optimism not only in users’ perceptions of information collection, but also on their self-efficacy in protecting their information in Facebook despite experiencing negative incidents in the past. To the best of our knowledge, this is the first study on the gap between users’ privacy perceptions around Facebook apps and the reality. The findings from this study offer directions for future research to address that gap through designing usable, effective, and personalized privacy notices to help users to make informed decisions about using Facebook apps.more » « less
-
The European General Data Protection Regulation (GDPR) mandates a data controller (e.g., an app developer) to provide all information specified in Articles (Arts.) 13 and 14 to data subjects (e.g., app users) regarding how their data are being processed and what are their rights. While some studies have started to detect the fulfillment of GDPR requirements in a privacy policy, their exploration only focused on a subset of mandatory GDPR requirements. In this paper, our goal is to explore the state of GDPR-completeness violations in mobile apps' privacy policies. To achieve our goal, we design the PolicyChecker framework by taking a rule and semantic role based approach. PolicyChecker automatically detects completeness violations in privacy policies based not only on all mandatory GDPR requirements but also on all if-applicable GDPR requirements that will become mandatory under specific conditions. Using PolicyChecker, we conduct the first large-scale GDPR-completeness violation study on 205,973 privacy policies of Android apps in the UK Google Play store. PolicyChecker identified 163,068 (79.2%) privacy policies containing data collection statements; therefore, such policies are regulated by GDPR requirements. However, the majority (99.3%) of them failed to achieve the GDPR-completeness with at least one unsatisfied requirement; 98.1% of them had at least one unsatisfied mandatory requirement, while 73.0% of them had at least one unsatisfied if-applicable requirement logic chain. We conjecture that controllers' lack of understanding of some GDPR requirements and their poor practices in composing a privacy policy can be the potential major causes behind the GDPR-completeness violations. We further discuss recommendations for app developers to improve the completeness of their apps' privacy policies to provide a more transparent personal data processing environment to users.more » « less
-
People value their privacy but often lack the time to read privacy policies. This issue is exacerbated in the context of mobile apps, given the variety of data they collect and limited screen space for disclosures. Privacy nutrition labels have been proposed to convey data practices to users succinctly, obviating the need for them to read a full privacy policy. In fall 2020, Apple introduced privacy labels for mobile apps, but research has shown that these labels are ineffective, partly due to their complexity, confusing terminology, and suboptimal information structure. We propose a new design for mobile app privacy labels that addresses information layout challenges by representing data collection and use in a color-coded, expandable grid format. We conducted a between-subjects user study with 200 Prolific participants to compare user performance when viewing our new label against the current iOS label. Our findings suggest that our design significantly improves users' ability to answer key privacy questions and reduces the time required for them to do so.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MASS56207.2022.00107
