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Award Detail

Doing Business As Name:Rutgers, The State University of New Jersey-RBHS-Robert Wood
  • Jerry Cheng
  • (732) 547-2755
Award Date:08/28/2014
Estimated Total Award Amount: $ 140,000
Funds Obligated to Date: $ 146,000
  • FY 2015=$6,000
  • FY 2014=$140,000
Start Date:09/15/2014
End Date:08/31/2017
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:EAGER: Collaborative Research: Towards Understanding Smartphone User Privacy: Implication, Derivation, and Protection
Federal Award ID Number:1449958
DUNS ID:078795875
Parent DUNS ID:001912864
Program:Secure &Trustworthy Cyberspace
Program Officer:
  • Sara Kiesler
  • (703) 292-8643

Awardee Location

Street:33 Knightsbridge Road
Awardee Cong. District:06

Primary Place of Performance

Organization Name:Rutgers, The State University of New Jersey-RBHS-Robert Wood
Street:125 Paterson Street
City:New Brunswick
County:New Brunswick
Cong. District:06

Abstract at Time of Award

This project aims to address privacy concerns of smartphone users. In particular, it investigates how the usages of the smartphone applications (apps) may reshape users' privacy perceptions and what is the implication of such reshaping. There has been recent work that investigates privacy leakage and potential defense mechanisms. However, so far there is only limited understanding on the consequences of such privacy losses, especially when large amount of privacy information leaked from smartphone users across many apps. The project seeks to investigate how the mobile technology (i.e., smartphone and smartphone apps) can reveal users' personal information and identify the consequences of privacy violations, by taking users' social relationships into consideration. The project facilitates a deep understanding of user privacy in the age of mobile devices and further develops appropriate protective mechanisms. Smartphone user privacy across different levels are analyzed including individual, social and community relationships based on different levels of information leakage. Statistical models, such as Bayesian networks and hidden Markov models, are developed to understand users' temporal privacy leakage patterns based on large-scale trace-driven investigation and experimental study. Data visualization tools are developed to capture and display the spatial-temporal patterns and summary statistics of different types of privacy leakage in real time, which helps users gain better insights on potential privacy losses. The statistical modeling and the data visualization techniques further enable the social scientists to study the psychological or social consequences of privacy violations, and identify factors encouraging attention or inattention to smartphone user privacy.

Publications Produced as a Result of this Research

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Jerry Q. Cheng, Regina Liu, Min-ge Xie "Fusion learning" Wiley: StatsRef, v., 2017, p..

Jerry Cheng, Tianhao Luo, and Minge Xie "A Novel Joint Frailty Model for Both Event and Failure Time" Third International Workshop on Recurrent Event Data Analysis, v., 2016, p..

Jian Liu, Yan Wang, Yingying Chen, Xu Chen and Jerry Cheng "Fine-grained Sleep Monitoring Through Off-the-Shelf WiFi, IEEE/ACM Transactions on Networking" IEEE/ACM ToN, v., 2017, p..

Project Outcomes Report


This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This proposal aims to investigate one important aspect of the advertisers’ perspective, how much an advertiser may infer about users’ social and community relationships by combining data from multiple applications and across many users. The key insights of the project include: (1) developing learning models on the social and community relationship inference process in a multi-layer framework; (2) conducting real-life experiments with participants of family, colleague and circles of friends, using various apps in their daily lives; analyzing to what extent an advertiser can infer the relationships by aggregating data from multiple apps and across users; (3) developing statistical models for users’ temporal privacy leakage patterns based on the experimental study and large-scale trace-driven investigation; and (4) building a visualization tool that captures and displays the spatial-temporal patterns and summary statistics of different types of privacy leakage in real time, which helps users gain better insights on the scope and degree of privacy losses.

This project results in multiple publications centered around (1) Understanding the Advertiser's Perspective of Smartphone User Privacy, (2) Understanding the Leakage of Social Relationships and Demographics from Surrounding APs, and (3) Protecting Multi-Lateral Localization Privacy in Pervasive Environments.

(1) Understanding the Advertiser's Perspective of Smartphone User Privacy: In this work, we quantify to what extent an advertiser can learn and infer users’ relationships by developing a privacy leakage inference framework. Our systematic study on privacy leakage inference involves both real experiments with multiple volunteers as well as trace-driven studies with human mobility traces obtained from two data sets, namely MIT reality trace and Foursquare trace. By examining the privacy leakages of participants from a diverse background ranging from academia to city environments (i.e., our real experiments and the MIT trace are academia whereas the Foursquare trace represents a city environment), we discover that the privacy leakage enables an advertiser to infer a significant portion of a user’s real world relationships that have physical interactions.

(2) Understanding the Leakage of Social Relationships and Demographics from Surrounding APs: While the mobile users enjoy the anytime anywhere Internet access by connecting their mobile devices through Wi-Fi services, the increasing deployment of access points (APs) have raised a number of privacy concerns. This work explores the potential of smartphone privacy leakage from surrounding APs. In particular, we study to what extent the users’ personal information such as social relationships and demographics could be revealed leveraging simple signal information from APs without examining the Wi-Fi traffic. Our approach utilizes users’ activities at daily visited places derived from the surrounding APs to infer users’ social interactions and individual behaviors. Furthermore, we develop two new mechanisms: Closeness-based Social Relationship Inference algorithm captures how closely people interact with each other by evaluating their physical closeness to derive fine-grained social relationships, whereas the Behavior-based Demographics Inference method differentiates various individual behaviors via the extracted activity features (e.g. activeness and time slots) to reveal users’ demographics.

(3) Protecting Multi-Lateral Localization Privacy in Pervasive Environments: Location based services (LBSs) have raised serious privacy concerns in the society, due to the possibility of leaking a mobile user’s location information in enabling location-dependent services. In this work, we study the multi-lateral privacy preserving localization problem, whereby the location of a target is calculated without the need of revealing anchors’ location, and the knowledge of the localization outcome, i.e., the target’s location, is strictly limited to the target itself. To fully protect user’s privacy, our study protects not only the user’s exact location information (the geo-coordinates), but also any side information that may lead to a coarse estimate of the location. We formulate the problem as a secure least-squared-error (LSE) estimation for an over-determined linear system, and develop three privacy-preserving solutions by leveraging combinations of information hiding and homomorphic encryption. These solutions provide different levels of protection for location side information and resilience to node collusion, and have the advantage of being able to trade user’s privacy requirements for better computation and communication efficiency.

Broader impacts of this project include the collaboration with industry, department weekly seminar, three revised or newly developed data mining and big data analysis courses, training for 1 PhD student, 1 MS student, and 1 undergraduate. Students in the courses of ST641 "Analytic for business intelligence", BIST0650 "Applied longitudinal data analysis", and BIST0690 "Advanced topics in biostatistics - data mining and big data analytics" are excellent hd the opportunity to conduct course projects which are related to the research tasks in this project. 


Last Modified: 10/30/2017
Modified by: Jerry Cheng

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