skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: I Don't Know Why You Need My Data: A Case Study of Popular Social Media Privacy Policies
Data privacy, a critical human right, is gaining importance as new technologies are developed, and the old ones evolve. In mobile platforms such as Android, data privacy regulations require developers to communicate data access requests using privacy policy statements (PPS). This case study cross-examines the PPS in popular social media (SM) apps---Facebook and Twitter---for features of language ambiguity, sensitive data requests, and whether the statements tally with the data requests made in the Manifest file. Subsequently, we conduct a comparative analysis between the PPS of these two apps to examine trends that may constitute a threat to user data privacy.  more » « less
Award ID(s):
1850054
PAR ID:
10353978
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ACM CODASPY 2022
Page Range / eLocation ID:
340 to 342
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The dominant privacy framework of the information age relies on notions of “notice and consent.” That is, service providers will disclose, often through privacy policies, their data collection practices, and users can then consent to their terms. However, it is unlikely that most users comprehend these disclosures, which is due in no small part to ambiguous, deceptive, and misleading statements. By comparing actual collection and sharing practices to disclosures in privacy policies, we demonstrate the scope of the problem. Through analysis of 68,051 apps from the Google Play Store, their corresponding privacy policies, and observed data transmissions, we investigated the potential misrepresentations of apps in the Designed For Families (DFF) program, inconsistencies in disclosures regarding third-party data sharing, as well as contradictory disclosures about secure data transmissions. We find that of the 8,030 DFF apps (i.e., apps directed at children), 9.1% claim that their apps are not directed at children, while 30.6% claim to have no knowledge that the received data comes from children. In addition, we observe that 10.5% of 68,051 apps share personal identifiers with third-party service providers, yet do not declare any in their privacy policies, and only 22.2% of the apps explicitly name third parties. This ultimately makes it not only difficult, but in most cases impossible, for users to establish where their personal data is being processed. Furthermore, we find that 9,424 apps do not use TLS when transmitting personal identifiers, yet 28.4% of these apps claim to take measures to secure data transfer. Ultimately, these divergences between disclosures and actual app behaviors illustrate the ridiculousness of the notice and consent framework. 
    more » « less
  2. As data privacy continues to be a crucial human-right concern as recognized by the UN, regulatory agencies have demanded developers obtain user permission before accessing user-sensitive data. Mainly through the use of privacy policies statements, developers fulfill their legal requirements to keep users abreast of the requests for their data. In addition, platforms such as Android enforces explicit permission request using the permission model. Nonetheless, recent research has shown that service providers hardly make full disclosure when requesting data in these statements. Neither is the current permission model designed to provide adequate informed consent. Often users have no clear understanding of the reason and scope of usage of the data request. This paper proposes an unambiguous, informed consent process that provides developers with a standardized method for declaring Intent. Our proposed Intent-aware permission architecture extends the current Android permission model with a precise mechanism for full disclosure of purpose and scope limitation. The design of which is based on an ontology study of data requests purposes. The overarching objective of this model is to ensure end-users are adequately informed before making decisions on their data. Additionally, this model has the potential to improve trust between end-users and developers. 
    more » « less
  3. The prevalence of smartphones in our society warrants more research on understanding the characteristics of users and their information privacy behaviors when using mobile apps. This paper investigates the antecedents and consequences of “power use” (i.e., the competence and desire to use technology to its fullest) in the context of informational privacy. In a study with 380 Android users, we examined how gender and users’ education level influence power use, how power use affects users’ intention to install apps and share information with them versus their actual privacy behaviors (i.e., based on the number of apps installed and the total number of “dangerous permission” requests granted to those apps). Our findings revealed an inconsistency in the effect of power use on users’ information privacy behaviors: While the intention to install apps and to share information with them increased with power use, the actual number of installed apps and dangerous permissions ultimately granted decreased with power use. In other words, although the self-reported intentions suggested the opposite, people who scored higher on the power use scale seemed to be more prudent about their informational privacy than people who scored lower on the power use scale. We discuss the implications of this inconsistency and make recommendations for reconciling smartphone users’ informational privacy intentions and behaviors. 
    more » « less
  4. 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
  5. Apple introduced privacy labels in Dec. 2020 as a way for developers to report the privacy behaviors of their apps. While Apple does not validate labels, they also require developers to provide a privacy policy, which offers an important comparison point. In this paper, we fine-tuned BERT-based language models to extract privacy policy features for 474,669 apps on the iOS App Store, comparing the output to the privacy labels. We identify discrepancies between the policies and the labels, particularly as they relate to data collected linked to users. We find that 228K apps' privacy policies may indicate data collection linked to users than what is reported in the privacy labels. More alarming, a large number (97%) of the apps with a Data Not Collected privacy label have a privacy policy indicating otherwise. We provide insights into potential sources for discrepancies, including the use of templates and confusion around Apple's definitions and requirements. These results suggest that significant work is still needed to help developers more accurately label their apps. Our system can be incorporated as a first-order check to inform developers when privacy labels are possibly misapplied. 
    more » « less