1. Presentation by Jun Hao Xu
The Price of Free Privacy leakage in personalized mobile in-app ads Authors: Wei Meng, Ren Ding, Simon P. Chung, Steven Han, and Wenke Lee
Presentation by Jun Hao Xu

2. Motivation
Mobile advertisements are crucial to the mobile app ecosystem
Win-win situation for both users and publishers.
But what are the hidden costs of mobile ads?
Mobile devices have become very intimate devices for us.
The risk of privacy leakage is a serious issue.

3. Background Ecosystem of Mobile Advertising
Publishers, Advertisers and Ad Networks
Targeting in Mobile Advertising
Topic Targeting
Interest Targeting
Demographic Targeting
Vulnerabilities
Mobile vs Web

4. Goals
How much information do ad networks know about users and how much of this is used?
Could personalized mobile in-app ads be served as a channel of private user information leakage?

5. Challenges Synthesized user profiles Removing external factors
Using synthesized user profiles leads to validity problems.
Real users were used to solve this.
Removing external factors
Geo-location is a important factor
Used VPN to hide location
Different apps may have different ad control
Developed own app

6. Methodology
Focus on Android phone and Google’s Admob for data collection.
Over 200 users were used for this study.
Install blank app dedicated for collecting mobile ads.
Blank app sends 100 ad requests.
Landing URL’s were used to represent the ads.
Ad’s were categorised into 1 of 24 categories.

7. Results
Interests
Mobile Ads are highly personalized based on user’s interests.
For 41% of users, more than 57% of their ad impressions match their real interests.
Google actively personalizing large fraction of its ads.

8. Results Demographic Gender Parental Status Income Religion
Google’s personalization algorithm
Religion
Result of other categories.
Age, Education, Ethnicity
Least targeted.

9. Privacy Leakage
The ability of observing personalized ads opens up a new avenue of attack on private personal information.
Demographics Learning from Personalized Mobile Ads
Machine learning algorithms to build user profiles based on ads seen.
Evaluated accuracy of user profiles
Dummy algorithm, which randomly guesses a user’s profile, used as a baseline classifier for comparison.

10. Results – Privacy Leakage
Classification algorithms performed better than dummy algorithms in all categories.
Potential risks involve selling information or exploiting users.

11. Countermeasures Ad networks changing their policies
Add noise or randomness into the personalized results.
Providing coarser grained targeting options
Can’t expect all ad networks to follow this.
Find a way to isolate app and ad process.

12. Criticism - Positives Addresses problems of other research.
Introduces problem well and expands on it.

13. Criticism – Other Ad Networks
AdMob only used.
Other Ad Networks?
InMobi – Advertisements by basing the targeting on the users’ existing and previous applications instead of traditional metrics such as demographics or geography.

14. Criticism - Android Android only used in this experiment.
IOS or Windows phones.

15. Criticism – Geo Location
Geo Location isn’t considered.
Geo Location considered a significant factor.
Recruit more users.

16. Criticism – Small Sample Size
Only 217 Users recruited.
Introduces demographic bias
Larger data set means more reliable data.

17. Criticism – Methodology
Assumes that 100 requests will not alter the user profile significantly.
More Ad requests to confirm the validity of the results.

18. Questions?