Estimating Age Privacy Leakage in Online Social Networks

Transcription

1 Estimating Age Privacy Leakage in Online Social Networks Ratan Dey, Polytechnic Institute of New York University (NYU-Poly) IT Security for the Next Generation American Cup, New York 9-11 November, 2011

2 Motivation Third parties can ascertain private attributes by aggregating information Only 1.5% of 1.47M users reveal age. Whether it is possible to estimate the age of the remaining users i.e., those who aim to hide their ages with a high accuracy? Why Birth year? PAGE 2

3 Our contributions Age estimation Estimate the ages of 1.2M NYC Facebook users, based only on the limited profile information and friendship links provided in March 2010 dataset Develop a novel two step estimation methodology Exploit side information Exploit underlying social network structure Large Datasets July M active users, full profile pages, used as ground truth March M users, limited profile pages, want to estimate of 1.2M users Age Estimation for highly private users PAGE 3

4 Step by step age estimation Utilizing side information Step 0: Low hanging fruit - birth years publicly available. Step 1: Using high school graduation year BY = HSY mentions Step 2: Using Friends high school graduating classes mentions mentions PAGE 4

5 CS Vs Error level graph for step 1 & 2 Utilizing side information Using step 1, we can estimate Birth year for 94% of the users within error level 2 or less. Using step 2, we can estimate Birth year for 85% of the users within error level 2 or less. PAGE 5

6 Summary of results from step 0,1,2 Utilizing side information Let G = {1.2M users, want to estimate ages} Let H = G 0 U G 1 U G 2 Set # NYC users % of NYC users # Ground truth users % of Ground truth users MAE on Ground truth users CS(4) on Ground truth users G 0 15, % 8, % 0 100% G 1 215, % 98, % % G 2 453, % 141, % % H 685, % 248, % % PAGE 6

7 Step 3: Iterative method utilizing social links Initialization (Iteration #0)? C A? B? PAGE 7

8 Step 3: Iterative method utilizing social links Iteration #1? C Assigned ages in iteration #1 PAGE 8

9 Step 3: Iterative method utilizing social links Iteration #2 Assigned ages in iteration #2 x u (i+1) = α x u (i) + (1 α)φ[x v (i), v in F u (i)] BY = MEAN MEDIAN STD or percentiles PAGE 9

10 Reverse Friend Lookup Estimating ages of highly private users For 46.3% of these users we can find at least 15 (NYC) friends. PAGE 10

11 Defenses for the Age Privacy Attack User can configure her privacy settings so that age, high-school graduation year, and friend lists are not available in her limited profile (that is, to non-friends). Reverse lookup can also be potentially used to infer not only age, but also other attributes including religious & political preferences. To prevent reverse friend lookup Please hide me from friends friend lists too PAGE 11

12 Conclusion We investigated how difficult is it to estimate the ages of OSN users who do not reveal their ages publicly. We develop a novel two step procedure Exploit side information like high school graduation year or high school graduation year of friends Exploit the underlying social network structure to develop an iterative algorithm Iterative method can be potentially used to infer not only age, but also other attributes. Our overall methodology able to estimate the ages of 84% of the NYC users with a 4 year mean absolute error. It is very hard for a user to avoid privacy leakages, even if the user takes maximal measures to do so. Our work casts serious doubts on age privacy in OSNs. PAGE 12

13 Thank You Ratan Dey, Polytechnic Institute of New York University (NYU-Poly) IT Security for the Next Generation American Cup, New York 9-11 November, 2011