PRIVACY-AWARE Personalization FOR Mobile Advertising.

1. PRIVACY-AWARE Personalization FOR Mobile Advertising. Michaela HardtSumanNath Presented By JyothiKallam

2. Goals of the paper: • 1. Address the problem of personalizing ad delivery to smart phone. • 2.Optimization of ad-delivery by protecting privacy and efficiency is NP-Hard. • 3.Effecient algorithm with tight approximation guarantee. • 4.First-differential private distributed protocol to compute statistics in presence of dynamic and malicious nodes. • 5. Experiments on real click logs achieved efficiency, privacy and ad relevance simultaneously.

3. Personalized Advertising ??? • Online advertisers uses users contexts and activities along with browsing and click history. • Personalization-identifies the user and his activities. • No Privacy.

4. PersonalizedAdDeliverysystem: • 1.Private user data:- statistics gathering • 2.Ad delivery:- select the best ad • 3.billing advertisers:-collect money for clicks.

5. Privacy-awareaddelivery • Repriv– controlled data sharing with the server, personalization based on limited data at the server-side. • Privad- Places a proxy between server and client, to achieve anonymity. • Delivering personalized ads from server to client is an optimization problem with variables: privacy, communication efficiency, utility( in terms of revenue and relevance). • Server only solution achieves optimal efficiency at the cost of privacy or utility. • Client only solution ensures optimal privacy but sacrifices efficiency or utility. • Trusted third party is required!!!! • Need for hybrid framework –personalization jointly by ad server and client.

6. Privacy-preservingstatisticsgathering • Personalized ad’s chosen based on historical information, and context clicked i.e., context-dependent click-through rates (CTRs). • Users unwilling to reveal their context and click information. • A novel aggregation protocol to compute CTRs from a highly dynamic population without trusted server.

7. Framework: • Three classes of participants: 1.Users 2.Advertisers 3.ad-service provider • It works in two parallel phases: 1.Statistics Gathering (historical data, CTR) 2.Ad-delivery (current context) Privacy up to some extent can be achieved with the help of generalization.

8. Server computes CTRs of all devices offline. • CTR of an ad is no of clicks on the ad divided by the number of times it is shown. • Based on limited values, server delivers two ads to client, but client phone displays one appropriate ad.

9. Desiderata: Desiderata for Ad Delivery: • Privacy • Efficiency • Revenue and Relevance Desiderata for Statistic Gathering: 1.Privacy in the absence of a trusted server 2.Scalability 3.Robustness to a Dynamic User Population

10. Privacy-awareaddelivery 1.The P-E-R Trade-offs • Optimizing three goals without trusted third party is difficult. • Dropping any one of the design goal, makes the system easy. • Expected revenue is given by pa.CTR(a c).

11. 2.Optimizing Ad Delivery: Based on the context information, server selects some ‘k’ ads from ‘A’ ad set that are sent to the user. Choose ad in such a way it maximizes revenue. • Client side Computation • Server side Computation Select A* of k ads from A that maximizes the expected revenue in generalized context c’

12. 3.Ad Selection Algorithm: In a generalized context c’ it is NP hard to select the revenue-maximizing set of k ads A*. Approximation Algorithm:

13. Privatestatisticsgathering: • Mechanism employed to build a scalable and robust protocol by using a server and a proxy. • Server is responsible for key distribution and the computation of final result. • Proxy is responsible for aggregation and anonymization. • Probability distribution over context pr[c], context dependent click through rates CTR(a|c) are estimated by counting how many users were in a specific context c and viewed/clicked on a specified ad a.

14. Assumptions • Honest-but-Curious Servers • Honest Fraction of Users • Using ɛ-differential privacy, which works even without the contexts and clicks of users. • In the absence of server( trusted third party), we generate noise required to ensure differential privacy in a distributed manner. With the help of probabilistic relaxation (ɛ, ɤ) differential privacy.

15. PrivacyPreservingDistributedcount • Counting Protocol:

16. Privacy-preservingestimates

17. Top-Downcomputationofnoisystatistics

18. Top-DownAlgorithm • To compute privacy-preserving CTRs for the generalized contexts in the hierarchy H. • Starts at the root and traverses through node v for each add a. • Estimates CTR(a,v) by calling Count to compute clicks of descendants on ad a. • If the count is above min_support then the algorithm recurses on v’s children ,otherwise all descendants are pruned. Privacy and efficiency of Algorithm: O( a+branch(H)).height(H).N.m/min_support)

19. experiments • Setup- race of location aware schemes, where each trace has a schema: <user-ID,query,user_location,business-ID> • Context attributes: location, interest, Query. • Attribute generalization: (x,y,z) denotes (Level-x location, level-y interest, level-z query). • Context Hierarchy • Generalization provides privacy and helps personalization with sparse data. • Metrics: Precision and coverage • The higher the precision and coverage values , the better the performance.

24. Conclusion • Addressed the problem of personalizing ad delivery without violating privacy. • Problem of selecting the most relevant ads under constraints on privacy and efficiency is NP Hard. • Proposed differentially private distributed protocol. • Achieving privacy, efficiency, ad relevance simultaneously.