Privacy-Aware Personalization for Mobile Advertising Krishna Rawali Puppala Privacy-Aware Personalization for Mobile Advertising Michaela Hardt Suman Nath
Summary Contextual Computing Personalized Ad Delivery System Framework Ad selection Algorithms Distributed Count Protocol Experimental Set-up Conclusion
Contextual Computing
Personalized ad Delivery System Statistics gathering
Personalized ad Delivery system
Personalized ad Delivery System Billing Advertisers
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
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
Framework Users who are served ads Advertisers who pay for clicks on their ads Ad service provider who decides which ads to display
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
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
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.
Greedy Algorithm
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.
Privacy Preserving Distributed Count Counting Protocol
Privacy Preserving Estimates Top-Down Computation
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
Experimental Setup Attribute Generalization Context Hierarchy Location Interest Query Context Hierarchy
Evaluating Trade-Offs Effect of CTR Threshold Effect of Communication Complexity
Evaluating Trade-Offs(Cont) Effect of Information Disclosure
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.