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.

移动应用程序经常使用分析基础架构来收集有关抚养性和用户行为的数据。存在大量的隐私问题，以期收集和使用此类数据。立法努力和森林要求呼吁提高透明度，并在数据收集的效用与对相应的隐私权丧失之间的妥协。该项目将通过采用差异隐私来改善移动应用程序分析的隐私，该隐私提供了严格的理论和强大的算法框架，以实现保留性能分析。研究和教育的整合将发展移动应用程序和数据分析的开发人员的专业知识。招募代表性不足的学生将归类于计算的多样性。该项目将在用户对隐私的社会和立法要求的环境中的重要领域进行重要的领域。研究人员将通过利用本地差异隐私（LDP）的理论来开发移动应用分析中的严格和可量化的隐私机构。该项目的第一个贡献将成为Privaid的设计和实施，Privaid是一种概念方法，可以在广泛使用的移动应用程序分析中部署LDP分析的软件工具。基于此基础，针对重要软件分析的几种算法将使用新颖的技术进行随机化和抽样进行处理：（1）控制流图中的边缘/路径分析；（2）对事件分析和图形分析的重金运输者的分析；（3）分析不连续的观察。公众发布Privaid，SoftwareAlyses和实验主题将使未来的LDP软件分析的开发和评估。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来通过评估来获得支持的。

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