The Promise of Differentially PrivateSocial Network Analysis

Vishesh Karwa

Carnegie Mellon University

LARC – March 27th 2015

What is Statistical Privacy?

Overview of Privacy Research

Sharing Networks data privately

In This Talk

Why is Privacy Important?

7

Three methods for sharing data

www.sangrea.net

MechanismDatabase of

People/RelationsUsers

f(G)

queries

answers

Government,researchers,businesses

(or) Maliciousadversary

Privacy in Statistical Databases

8

Why is Privacy Hard?

Why is Network Privacy Hard?

Attacks on Past Techniques

Picture from Andreas Haeberlen’s slides

Netflix attack [Narayanan, Shmatikov 2008]

Picture Courtesy – Adam Smith and Arvind Narayanan

Lessons Learned

In This Talk

The Cryptographic Solution to Privacy

P(Z|X) P(Z|X’)

x x’

Edge Differential Privacy*

x

x’

P(.|x) P(.|x’)

Differential Privacy - Properties

I am okay with giving my data for the study, but I need to protect

my privacy.

Don’t worry, no one will learn anything more about you than what they already know.

The Differential Privacy Guarantee

How to achieve Differential Privacy?

Global Sensitivity:

Example - Laplace Mechanism

20

Laplace Mechanism:

f(G) f(G’)

In This Talk

Key issues with Differential privacy*

G

G

An ERGM framework for networks

The Beta model

1

2

5

3

4

Private estimator of beta model

Step 1 - Release degree sequence

Step 2 - Re-estimate Degree Sequence

Step 3 - Estimate parameters

Karate Data Set

Likoma n=250, m = 248 Degree sequence of people on Likoma Island

Karate n = 34, m = 78 Network of Members of Karate club

Likoma Island

Likoma n=250, m = 248 Degree sequence of people on Likoma Island

Karate n = 34, m = 78 Network of Members of Karate club

More general ERGMs…

Randomized ResponseOld Wine in new Bottle

Inference with Randomized data

Approximate Likelihood Inference

Faux Mesa High

Teenage friendship study

KL divergence

Faux Mesa High

Teenage Friendship Data

Summary

Thanks!

Questions?