krishna rawali puppala privacy-aware personalization for mobile advertising michaela hardt suman...
TRANSCRIPT
Krishna Rawali Puppala
Privacy-Aware Personalization for Mobile Advertising
Michaela HardtSuman 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
Ad delivery
Personalized ad Delivery System
Billing Advertisers
Privacy Aware Ad Delivery
• Server only Personalization Repriv System
• Client only PersonalizationPrivad 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 participateThen 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 DeliveryPrivacyEfficiencyRevenue & Relevance
Expected revenue is
• Goals for Statistic GatheringPrivacy in the absence of trusted serverScalabilityRobustness
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– 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.