1. Privacy Leakage in Personalized Mobile In-App Ads
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
Presented By – Jordan Wong

2. Motivation Mobile ads are essential to the mobile app ecosystem
Using personalized ads have a higher success rate
But what are the hidden costs of mobile ads?
Our mobile devices are very intimate devices
Privacy leakage is a serious issue

3. Background Mobile ecosystem Types of targeting
App developer
Advertiser
Ad network
Types of targeting
Topic targeting – ad and app contain the same topics
Interest targeting – user interested in the ad content
Demographic targeting – ad content appeals to a demographic
Mobile vs web advertising
Lack of isolation in mobile advertising

4. Purpose of this paper Analyzing ad networks
How much user information do ad networks know
How much of this is used in ad personalization
Accuracy of the personalization algorithm
Are mobile ads a new channel of privacy leakage?
What can be learnt from analyzing the ads someone receives

5. Challenges How to simulate real users in the experiment?
Recruit real people to conduct the experiment
Geo-location is a very dominant factor in ad personalization
Use a Virtual Private Network (VPN) to mask location
All requests originate from same IP address

6. Methodology Using Android App market ( > 75% market share)
Using Google’s AdMob ( 35% market share)
More than 200 Android users were recruited
Provide information about themselves
Install and run the research app

7. App Design Makes 100 ad requests without any ad control
Collects the ads received without clicking on them
Landing URL’s were used
Collected ads categorized

8. Results – Interest targeting
Mobile ads are highly personalized based on user’s interest
More than 40% of users had 60% of the ads received match their real interest

9. Results – Demographic targeting
Biggest factor is gender
2nd is parental status
3rd is income
Personalization algorithm considers this, not the advertisers
Age, ethnicity and education have lowest impact

10. Privacy Leakage Why is this a problem?
Exploiting your personal information
Used machine learning classification algorithms
Build user profile from ads they received
Used dummy algorithm as base line
Randomly guesses user profiles

11. Reconstruction Results
Classification algorithms performed better than dummy algorithm in ALL categories
Easier to derive user profiles using the information about their ads

12. Countermeasures Isolate app and ad process
Reduce effectiveness of ad networks personalization algorithm
Unlikely all ad networks will do this
Use HTTPS
Does not solve problem

13. Criticism - Negatives No validation done on participants Ethics
Their user profiles
Demographic information they provided
Ethics
Advertisers paying for this experiment

14. Criticism - Positives Addresses weaknesses in previous research
Aware of some of their weaknesses
Consider geo-location in the future
Use more ad networks

15. Thank you
Q & A