A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Targeted Advertising ... And Privacy Too
CT-RSA 2001 Proceedings of the 2001 Conference on Topics in Cryptology: The Cryptographer's Track at RSA
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Towards Privacy-Aware Location-Based Database Servers
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Privacy-enhancing personalized web search
Proceedings of the 16th international conference on World Wide Web
Supporting anonymous location queries in mobile environments with privacygrid
Proceedings of the 17th international conference on World Wide Web
Composition and Generalization of Context Data for Privacy Preservation
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Location Diversity: Enhanced Privacy Protection in Location Based Services
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Approximate Evaluation of Range Nearest Neighbor Queries with Quality Guarantee
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Location Privacy Techniques in Client-Server Architectures
Privacy in Location-Based Applications
A utility-theoretic approach to privacy and personalization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Darwin phones: the evolution of sensing and inference on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
The good, the bad, and the random: an eye-tracking study of ad quality in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Differentially-private network trace analysis
Proceedings of the ACM SIGCOMM 2010 conference
Empirical models of privacy in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Challenges in measuring online advertising systems
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Privacy Violations Using Microtargeted Ads: A Case Study
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
RePriv: Re-imagining Content Personalization and In-browser Privacy
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Transactions on Data Privacy
I have a DREAM!: differentially private smart metering
IH'11 Proceedings of the 13th international conference on Information hiding
Preserving user location privacy in mobile data management infrastructures
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Towards statistical queries over distributed private user data
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
SmartAds: bringing contextual ads to mobile apps
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
SplitX: high-performance private analytics
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Hi-index | 0.00 |
Mobile advertising is an increasingly important driver in the Internet economy. We point out fundamental trade-offs between important variables in the mobile advertisement ecosystem. In order to increase relevance, ad campaigns tend to become more targeted and personalized by using context information extracted from user's interactions and smartphone's sensors. This raises privacy concerns that are hard to overcome due to the limited resources (energy and bandwidth) available on the phones. We point out that in the absence of a trusted third party, it is impossible to maximize these three variables - ad relevance, privacy, and efficiency - in a single system. This leads to the natural question: can we formalize a common framework for personalized ad delivery that can be instantiated to any desired trade-off point? We propose such a flexible ad-delivery framework where personalization is done jointly by the server and the phone. We show that the underlying optimization problem is NP-hard and present an efficient algorithm with a tight approximation guarantee. Since tuning personalization rules requires implicit user feedback (clicks), we ask how can we, in an efficient and privacy-preserving way, gather statistics over a dynamic population of mobile users? This is needed for end-to-end privacy of an ad system. We propose the first differentially-private distributed protocol that works even in the presence of a dynamic and malicious set of users. We evaluate our methods with a large click log of location-aware searches in Microsoft Bing for mobile. Our experiments show that our framework can simultaneously achieve reasonable levels of privacy, efficiency, and ad relevance and can efficiently support a high churn rate of users during the gathering statistics that are required for personalization.