Timezone: »

 
Invited talk 3: Challenges in the Privacy-Preserving Analysis of Structured Data
Kamalika Chaudhuri

Sat Dec 08 10:30 AM -- 11:20 AM (PST) @
in Privacy Preserving Machine Learning »

Much data analysis today is done on sensitive data, and particular privacy challenges arise when this data is sensitive and structured. In this talk I will describe two such challenges in the privacy-preserving analysis of complex, structured data that we have been working on in my group.

The first is continual release of graph statistics in an online manner from an expanding graph, which is motivated by a problem in HIV epidemiology. Even though node differentially private release of graph statistics is highly challenging, here we will describe how we can get a differentially private solution for this problem that performs better than the natural sequential composition baseline.

Next, I will talk about analysis of sensitive structured, correlated data, while still preserving the privacy of events in the data. Differential privacy does not adequately address privacy issues in this kind of data, and hence will look at a form of inferential privacy, called Pufferfish, that is more appropriate. We will provide mechanisms, establish their composition properties, and finally evaluate them on real data on physical activity measurements across time.

Author Information

Kamalika Chaudhuri (UCSD)

More from the Same Authors