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SHF: Small: PrivAid: Differentially-Private Analytics for Android Apps

SHF：小型：PrivAid：Android 应用程序的差分隐私分析

基本信息

批准号：
1907715
负责人：
Atanas Rountev
金额：
$ 49.99万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907715&HistoricalAwards=false
关键词：
SHF Small PrivAid Differentially Private

项目摘要

Mobile apps often use analytics infrastructures to gather data on appperformance and user behaviors. There are significant privacy concernsabout the collection and use of such data. Legislative efforts andsocietal demands are calling for increased transparency andwell-defined compromises between the utility of data gathering and thecorresponding loss of privacy. This project will improve the privacyof mobile app analytics by employing differential privacy, whichprovides a rigorous theory and a powerful algorithmic framework forprivacy-preserving analysis. Integration of researchand education will develop the expertise of developers of mobile appsand data analyses. Recruitment of underrepresented students willcontribute to increased diversity in computing. The project willadvance the important area of user privacy, in an environment withrapidly-increasing societal and legislative demands for privacy.The investigators will develop rigorous and quantifiable privacymechanisms in mobile-app analytics by leveraging the theory of localdifferential privacy (LDP). The first contribution of the project willbe the design and implementation of PrivAid, a conceptual approach andrelated software tools for deploying LDP analyses in widely-usedinfrastructures for mobile-app analytics. Based on this foundation,several algorithms targeting important software analyses will bedeveloped using novel techniques for randomization and sampling: (1)edge/path profiling in control-flow graphs; (2) analyses of heavyhitters for event profiling and graph profiling; (3) profiling undercontinuous observation. The public release of PrivAid, softwareanalyses, and experimental subjects will enable the development andevaluation of future LDP software analyses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

移动的应用程序通常使用分析基础设施来收集有关应用程序性能和用户行为的数据。这些数据的收集和使用存在严重的隐私问题。立法努力和社会需求要求提高透明度，并在数据收集的效用和相应的隐私损失之间做出明确的妥协。该项目将通过采用差分隐私来改进移动的应用程序分析的隐私性，差分隐私为隐私保护分析提供了严格的理论和强大的算法框架。研究和教育的整合将发展移动的应用程序和数据分析开发人员的专业知识。招收代表性不足的学生将有助于增加计算的多样性。该项目将在社会和立法对隐私要求迅速增长的环境中推进用户隐私这一重要领域。研究人员将通过利用本地差异隐私（LDP）理论，在移动应用程序分析中开发严格和可量化的隐私机制。该项目的第一个贡献将是设计和实施PrivAid，这是一种概念方法和相关软件工具，用于在广泛使用的移动应用程序分析基础设施中部署LDP分析。在此基础上，我们将使用新的随机化和抽样技术，开发几种针对重要软件分析的算法：（1）控制流图中的边/路径分析;（2）事件分析和图分析的重量级分析;（3）连续观察下的分析。PrivAid、软件分析和实验对象的公开发布将使未来LDP软件分析的开发和评估成为可能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A study of event frequency profiling with differential privacy

DOI：
10.1145/3377555.3377887
发表时间：
2020-02
期刊：
Proceedings of the 29th International Conference on Compiler Construction
影响因子：
0
作者：
Hailong Zhang;Yu Hao;S. Latif;Raef Bassily;A. Rountev
通讯作者：
Hailong Zhang;Yu Hao;S. Latif;Raef Bassily;A. Rountev

Introducing Differential Privacy Mechanisms for Mobile App Analytics of Dynamic Content

DOI：
10.1109/icsme46990.2020.00034
发表时间：
2020-09
期刊：
2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)
影响因子：
0
作者：
S. Latif;Yu Hao;Hailong Zhang;Raef Bassily;A. Rountev
通讯作者：
S. Latif;Yu Hao;Hailong Zhang;Raef Bassily;A. Rountev

Introducing Privacy in Screen Event Frequency Analysis for Android Apps

DOI：
10.1109/scam.2019.00037
发表时间：
2019-09
期刊：
2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)
影响因子：
0
作者：
Hailong Zhang;S. Latif;Raef Bassily;A. Rountev
通讯作者：
Hailong Zhang;S. Latif;Raef Bassily;A. Rountev

Differential privacy for coverage analysis of software traces

软件痕迹覆盖分析的差分隐私