PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps
Abstract
Smartphone app developers often access and use privacy-sensitive data to create apps with rich and meaningful interactions. However, it can be challenging for auditors and end-users to know what granularity of data is being used and how, thereby hindering assessment of potential risks. Furthermore, developers lack easy ways of offering transparency to users regarding how personal data is processed, even if their intentions are to make their apps more privacy friendly. To address these challenges, we introduce PrivacyStreams, a functional programming model for accessing and processing personal data as a stream. PrivacyStreams is designed to make it easy for developers to make use of personal data while simultaneously making it easier to analyze how that personal data is processed and what granularity of data is actually used. We present the design and implementation of PrivacyStreams, as well as several user studies and experiments to demonstrate its usability, utility, and support for privacy.
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As pointed out by existing work in LLMs and natural language processing, there are restrictions to the number of tokens input to the LLMs, limiting the length of conversation history texts that could be included in LLM prompting.
“As pointed out by existing work in LLMs and natural language processing, there are restrictions to the number of tokens input to the LLMs [6], limiting the length of conversation history texts that could be included in LLM prompting [12,70,116].”
From Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older Adults (2024) · cites this paper as [70]
Cites
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Self-citationMoodScope: building a mood sensor from smartphone usage patterns
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services · doi:10.1145/2462456.2464449 · Cited by 444 · see citing works
AWARE: Mobile Context Instrumentation Framework
Ferreira, D., Kostakos, V., & Dey, A. K. (2015) · Frontiers in ICT · doi:10.3389/fict.2015.00006 · Cited by 409 · see citing works
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Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi
Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., & Yang, Z. (2019) · Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326081 · Cited by 557 · see citing works
Yang, Z., Xu, X., Yao, B., Rogers, E., Zhang, S., Intille, S., Shara, N., Gao, G. G., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3659625 · Cited by 120 · see citing works
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
Li, Y., Hua, J., Wang, H., Chen, C., & Liu, Y. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) · doi:10.1109/icse43902.2021.00035 · Cited by 75 · see citing works
Self-citationRescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots
Zhou, J., Xu, E., Wu, Y., & Li, T. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713701 · Cited by 17 · see citing works
Self-citation
- Authors
- Li, Y., Chen, F., Li, T. J.-J., Guo, Y., Huang, G., Fredrikson, M., Agarwal, Y., & Hong, J. I.
- Affiliations
- Peking University (CN)
- Carnegie Mellon University (US)
- Carnegie Mellon University, USA
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2017-09-11
- Volume
- 1
- Issue
- 3
- Pages
- 1-26
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2756371034
- Cited by
- 39
- References
- 3
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 3 · 2022: 4 · 2021: 7 · 2020: 7 · 2019: 5 · 2018: 7
- FWCI
- 13.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Funders
- National Natural Science Foundation of China (61421091)
- China Scholarship Council (201606010240)
- National Science Foundation (CSR-1526237, CNS-1564009)
- Air Force Research Laboratory (FA8750-15-2-0281)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Keywords
- privacy-preserving data processing, mobile application, data granularity, privacy transparency, privacy auditing, data access control, user privacy
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