1. The Price of Free Privacy Leakage in Personalized Mobile In-App Ads
Article Authors – Wei Meng, Ren Ding, Simon P. Chung, Steven Han, and Wenke Lee
Presentation By – Ammar Bagasrawala

2. Motivation
Advertisements are an essential part to the mobile app ecosystem
By providing personalized ads, ad networks can increase the effectiveness of the ads they supply
How much data are users giving away to pay for “free apps”?
Our mobile phones have become very intimate devices
Privacy leakage is a serious issue in the mobile ecosystem

3. Background Ecosystem of mobile advertising
Publishers, advertisers, and ad networks
Ad networks can obtain information about users and are responsible for generating user profiles
Targeting in mobile advertising
Topic targeting – ad content related to application content
Interest targeting – user has expressed interest in the topic the ad content contains
Demographic targeting – users within specific groups targeted
Lack of isolation for in-app advertising

4. The Price of Free Problem
Goals of this study
What personal information about users can a mobile ad provider know and use?
Could personalized mobile in-app ads be served as a channel of private information leakage?
Methodology
Focused on Android and Google’s mobile ad network AdMob
Surveyed over 200 people and collected data
Tested whether correlation existed between users and the ads they saw
Trained models to predict user information from ads
Results
Google’s ads are highly personalised based on interests and demographic information
User attributes can be predicted with accuracy

5. Data Collection Blank Android app
No ad control API
Sent requests from single IP address
Sends 100 ad requests to ad networks without setting any targeting attributes
Impact of experiment on the subjects ad profile was low
Landing URLs were used as the representation of ads in the analysis, and were categorized into 1 of 24 interest categories

6. Ad Personalization - Interests
Mobile ads are highly personalized based on user interests
For over 79% of the users, at least 21% of the categories in the ad interest profile are correct
For 11% of the users, at least 83% of the categories in the ad interest profile are correct
For 41% of the users, more than 57% of their ad impressions match their real interests
Google can not only build and use accurate interest profiles for users, but also that they are actively personalizing a large fraction of their ad deliveries.

7. Ad Personalization – Demographic Data
Gender
Parental status
User income
Advertisers not explicitly targeting income
Religion
Ads shown due to correlation with other categories
Political affiliation
No explicit evidence

8. Privacy Leakage Ads in Android are not isolated from developers
In web advertising, Iframes used
Demographics learning from personalized ads
Applied machine learning algorithms for predicting user information
Accuracy of predictions used as metric
Dummy, and augmented dummy classification models used as baseline

9. Privacy Leakage Results
At least 1 classifier performed better than the dummy’s for a category
Accuracy was higher for age, gender and parental status
Education, ethnicity, and religion do not have classifiers that perform significantly better than dummy’s

10. Article Criticism
Structure and flow of the article increased readability and understanding of key ideas
Experiment was controlled and allowed results to be reliable
Reasoning behind results and decisions made was clear
Related literature successfully used to enhance understanding of mobile ecosystem

11. Issues and Improvements
Small sample size and uneven demographic distribution of the data set
Data set should be cleaner and more distributed
Only looks at Google’s ad network
Analyse other ad networks to compare the security implications with Google’s
Geo-location is excluded from the study
Might be interesting to compare ads obtained with geo-location factored in

12. Questions?