1. Privacy-Aware Personalization for Mobile Advertising
Krishna Rawali Puppala
Privacy-Aware Personalization for Mobile Advertising
Michaela Hardt
Suman Nath

2. Summary Contextual Computing Personalized Ad Delivery System Framework
Ad selection Algorithms
Distributed Count Protocol
Experimental Set-up
Conclusion

3. Contextual Computing

4. Personalized ad Delivery System
Statistics gathering

5. Personalized ad Delivery system

6. Personalized ad Delivery System
Billing Advertisers

7. Privacy Aware Ad Delivery
Server only Personalization
Repriv System
Client only Personalization
Privad System
Can we formalize a common framework for personalized ad delivery that can be instantiated to any desired trade off point?
Hybrid Framework

8. Privacy-Preserving Statistics Gathering
Personalization information based on (Click-through rates) CTRs.
Problems : Users may or may not available during the course of stats gathering
Users might decline to participate
Then how can we achieve stats gathering in an efficient and privacy preserving way???
Ans- Developed a differentially private protocol without a trusted third party

9. Framework Users who are served ads
Advertisers who pay for clicks on their ads
Ad service provider who decides which ads to display

10. Desiderata Goals for Ad Delivery Goals for Statistic Gathering Privacy
Efficiency
Revenue & Relevance
Expected revenue is
Goals for Statistic Gathering
Privacy in the absence of trusted server
Scalability
Robustness

11. Privacy Aware Ad Delivery
The P-E-R Trade Offs
One has to find reasonable trade offs between the three design goals
Optimizing Ad Delivery
Client Side Computation
Server Side Computation

12. Ad Selection Algorithms
Client and Server can efficiently compute their parts of optimization jointly to choose the best set of ads that achieve a desired trade off.
Approximation Algorithm
Greedy Algorithm – Starts with A empty set and increments in each round that increases the expected revenue.
Provides maximum coverage problem.

13. Greedy Algorithm

14. Private Statistics Gathering
How to achieve statistics in a privacy way?
Uses Server and a Proxy
Server- Key distribution
Proxy- Aggregation and Anonymization
Ex – VeriSign as the proxy
Assumptions
Honest but curious servers
Honest Fraction of Users
Works with ε-differential privacy
Noise is generated in the absence of trusted third party. Probabilistic relaxation (ε,δ)-differential privacy is adopted.

15. Privacy Preserving Distributed Count
Counting Protocol

16. Privacy Preserving Estimates
Top-Down Computation

17. Experimental Setup
Dataset- Used a trace of location aware searches. Trace has a scheme : {user-ID, query, user-location, business-ID}
Context- Evaluation to contexts on
Location- User’s location
Interest- Multi set of Ids the user clicked on before
Query- The search query the user sends

18. Experimental Setup Attribute Generalization Context Hierarchy Location
Interest
Query
Context Hierarchy

19. Evaluating Trade-Offs
Effect of CTR Threshold Effect of Communication Complexity

20. Evaluating Trade-Offs(Cont)
Effect of Information Disclosure

21. Conclusion
Addressed the personalization ad delivery problem without compromising user privacy
Proposed the differentially private protocol
Computed statistics even in the presence of malicious users
Finally Achieved P-E-R.