Guest Speaker
Accurate Analysis of Large Private Datasets
Vibhor Rastogi
Dept. of Computer Science and Engineering
University of Washington
Feb 18 2010 3:30PM
480 Dreese Labs
All interested parties are welcome to attend.
Refreshments will be served prior to talk.
Abstract:
Today, no individual has full control over access to his personal information. Private data collected by various hospitals and universities, and also by websites like Google and Facebook, contain valuable statistical facts that can be mined for research and analysis, e.g., analyze outbreak of diseases, detect traffic patterns on the road, or understand browsing trends on the web, but concerns about individual privacy severely restricts its use, e.g., privacy attacks led AOL to recently pull-off its published search-log data.
To remedy this, much recent work focuses on data analysis with formal privacy guarantees. This has given rise to differential privacy considered by many as the golden standard of privacy. However, few practical techniques satisfying differential privacy exist for complex analysis tasks (e.g., analysis involving complex query operators), or new data models (e.g., data having temporal correlations or uncertainty). In this talk, I will discuss techniques that fill this void. I will first discuss a query answering algorithm that can handle joins (previously, no private technique could accurately answer join queries arising in many analysis tasks). This algorithm makes several privacy-preserving analyses over social network graphs possible for the first time. Then I will discuss a query-answering technique over time-series data, which enables private analysis of GPS traces and other temporally-correlated data. Third, I will discuss an access control mechanism for uncertain data, which enables enforcing security policies on RFID-based location data.
Finally, I will conclude by discussing some privacy and security problems in building next-generation computing systems based on new models for data (e.g., uncertain data), computing (e.g., cloud computing), and human computer interaction (e.g., ubiquitous systems).
Bio:
Vibhor Rastogi is a doctoral candidate in the Database group at the University of Washington. His dissertation develops techniques for privacy-preserving data analysis. His other research interests include data uncertainty, data cleaning, and problems in large-scale data management.
Host: Hakan Ferhatosmanoglu
* Vibhor Rastogi is a CSE faculty candidate
