Author image not provided
 Hongxia Jin

Authors:
Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article4.06
Citation Count69
Publication count17
Publication years2013-2017
Available for download14
Average downloads per article353.00
Downloads (cumulative)4,942
Downloads (12 Months)1,645
Downloads (6 Weeks)136
SEARCH
ROLE
Arrow RightAuthor only


AUTHOR'S COLLEAGUES
See all colleagues of this author

SUBJECT AREAS
See all subject areas




BOOKMARK & SHARE


17 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 17 of 17
Sort by:

1 published by ACM
October 2017 ACM Transactions on Privacy and Security (TOPS): Volume 20 Issue 4, October 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 39,   Downloads (12 Months): 102,   Downloads (Overall): 102

Full text available: PDFPDF
k -means clustering is a widely used clustering analysis technique in machine learning. In this article, we study the problem of differentially private k -means clustering. Several state-of-the-art methods follow the single-workload approach, which adapts an existing machine-learning algorithm by making each step private. However, most of them do not ...
Keywords: Differential privacy, k-means clustering, private data publishing

2
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

We introduced an adversarial learning framework for improving CTR prediction in Ads recommendation. Our approach was motivated by observing the extremely low click-through rate and imbalanced label distribution in the historical Ads impressions. We hence proposed a Disguise-Adversarial-Networks (DAN) to improve the accuracy of supervised learning with limited positive-class information. ...

3 published by ACM
October 2016 CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 13,   Downloads (12 Months): 522,   Downloads (Overall): 832

Full text available: PDFPDF
Recommender systems typically require users' history data to provide a list of recommendations and such recommendations usually reside on the cloud/server. However, the release of such private data to the cloud has been shown to put users at risk. It is highly desirable to provide users high-quality personalized services while ...
Keywords: privacy-preserving recommendation, privacy paradox, differential privacy

4 published by ACM
March 2016 CODASPY '16: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 43,   Downloads (12 Months): 353,   Downloads (Overall): 612

Full text available: PDFPDF
There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support ...
Keywords: differential privacy, private data publishing, k-means clustering

5 published by ACM
March 2016 IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 4,   Downloads (12 Months): 51,   Downloads (Overall): 152

Full text available: PDFPDF
Recent advancement of smart devices and wearable tech-nologies greatly enlarges the variety of personal data people can track. Applications and services can leverage such data to provide better life support, but also impose privacy and security threats. Obfuscation schemes, consequently, have been developed to retain data access while mitigate risks. ...
Keywords: privacy, user behavior, mobile, data obfuscation, tracking

6 published by ACM
February 2016 WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 4,   Downloads (12 Months): 71,   Downloads (Overall): 252

Full text available: PDFPDF
Fine-grained, personal data has been largely, continuously generated nowadays, such as location check-ins, web histories, physical activities, etc. Those data sequences are typically shared with untrusted parties for data analysis and promotional services. However, the individually-generated sequential data contains behavior patterns and may disclose sensitive information if not properly sanitized. ...
Keywords: sequential patterns, mutual information, data sanitization

7 published by ACM
October 2015 CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 6,   Downloads (12 Months): 52,   Downloads (Overall): 230

Full text available: PDFPDF
Mobile applications (Apps) could expose children or adolescents to mature themes such as sexual content, violence and drug use, which results in an inappropriate security and privacy risk for them. Therefore, mobile platforms provide rating policies to label the maturity levels of Apps and the reasons why an App has ...
Keywords: text mining, deep learning, pearson correlation, content rating, mobile apps, privacy risk, security risk

8 published by ACM
August 2015 MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 3,   Downloads (12 Months): 38,   Downloads (Overall): 181

Full text available: PDFPDF
Through a controlled online experiment with 447 Android phone users using their own devices, we investigated how empowering users with information-disclosure control and enhancing their ads awareness affect their installation behaviors, information disclosure, and privacy perceptions toward different mobile apps. In the 3 (control: no, low, high) x 2 (ads ...
Keywords: Notice and Consent, Privacy, Third-Party Applications (Apps), Android, Mobile, Ads Awareness, Control

9 published by ACM
May 2015 MobiSys '15: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services
Publisher: ACM
Bibliometrics:
Citation Count: 11
Downloads (6 Weeks): 6,   Downloads (12 Months): 64,   Downloads (Overall): 399

Full text available: MobiMobi  PDFPDF  ePubePub
The proliferation of mobile apps is due in part to the advertising ecosystem which enables developers to earn revenue while providing free apps. Ad-supported apps can be developed rapidly with the availability of ad libraries. However, today?s ad libraries essentially have access to the same resources as the parent app, ...
Keywords: ad libraries, app instrumentation, previlege de-escalation, mobile apps, static analysis

10 published by ACM
April 2015 ASIA CCS '15: Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 2,   Downloads (12 Months): 30,   Downloads (Overall): 154

Full text available: PDFPDF
We describe an architecture and a trial implementation of a privacy-preserving location sharing system called Albatross. The system protects location information from the service provider and yet enables fine-grained location-sharing. One main feature of the system is to protect an individual's social network structure. The pattern of location sharing preferences ...
Keywords: location privacy, privacy, private location sharing

11
December 2014 ICDM '14: Proceedings of the 2014 IEEE International Conference on Data Mining
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 2

Recommender systems become increasingly popular and widely applied nowadays. The release of users' private data is required to provide users accurate recommendations, yet this has been shown to put users at risk. Unfortunately, existing privacy-preserving methods are either developed under trusted server settings with impractical private recommender systems or lack ...
Keywords: Differential Privacy, Recommender System, Probabilistic Analysis, Data Perturbation, Learning and Optimization

12 published by ACM
November 2014 CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 3,   Downloads (12 Months): 57,   Downloads (Overall): 328

Full text available: PDFPDF
People have multiple accounts on Online Social Networks (OSNs) for various purposes. It is of great interest for third parties to collect more users' information by linking their accounts on different OSNs. Unfortunately, most users have not been aware of potential risks of such accounts linkage. Therefore, the design of ...
Keywords: multiple online social networks, experiments, computational hardness, information control mechanism, user accounts linkage inference, algorithms

13 published by ACM
April 2014 WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 3,   Downloads (12 Months): 19,   Downloads (Overall): 134

Full text available: PDFPDF
We study the first countermeasure against user identity linkage attack across multiple online social networks (OSNs). Our goal is to keep as much as user's information in public and meanwhile prevent their identities from being linked on different OSNs via k-anonymity. We develop a novel greedy algorithm, incorporating an efficient ...
Keywords: algorithm, countermeasure, user identity linkage attack

14 published by ACM
February 2014 IUI '14: Proceedings of the 19th international conference on Intelligent User Interfaces
Publisher: ACM
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 3,   Downloads (12 Months): 57,   Downloads (Overall): 511

Full text available: PDFPDF
Location-based systems are becoming more popular with the explosive growth in popularity of smart phones. However, the user adoption of these systems is hindered by growing user concerns about privacy. To design better location-based systems that attract more user adoption and protect users from information under/overexposure, it is highly desirable ...
Keywords: recommendation, location sharing, privacy, user behavior

15
December 2013 International Journal of Human-Computer Studies: Volume 71 Issue 12, December, 2013
Publisher: Academic Press, Inc.
Bibliometrics:
Citation Count: 16

In studies of people's privacy behavior, the extent of disclosure of personal information is typically measured as a summed total or a ratio of disclosure. In this paper, we evaluate three information disclosure datasets using a six-step statistical analysis, and show that people's disclosure behaviors are rather multidimensional: participants' disclosure ...
Keywords: Factor analysis, Information disclosure, Measurement, Privacy behavior, Latent class analysis, Structural equation modeling, Privacy attitude

16 published by ACM
October 2013 RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 5,   Downloads (12 Months): 95,   Downloads (Overall): 475

Full text available: PDFPDF
We present techniques to characterize which data contributes most to the accuracy of a recommendation algorithm. Our main technique is called differential data analysis. The name is inspired by other sorts of differential analysis, such as differential power analysis and differential cryptanalysis, where insight comes through analysis of slightly differing ...
Keywords: recommender systems

17 published by ACM
April 2013 CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher: ACM
Bibliometrics:
Citation Count: 18
Downloads (6 Weeks): 2,   Downloads (12 Months): 134,   Downloads (Overall): 580

Full text available: PDFPDF
We examine the effect of coarse-grained vs. fine-grained location sharing options on users' disclosure decisions when configuring a sharing profile in a location-sharing service. Our results from an online user experiment (N=291) indicate that users who would otherwise select one of the finer-grained options will employ a compensatory decision strategy ...
Keywords: decision making, information disclosure, privacy calculus, location-sharing services



The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us