Client Device Advertisement Auctions for Privacy
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Solution Overview
Problem
Conventional online advertising methods infringe on user privacy by collecting and analyzing personal data on server devices to deliver relevant advertisements, lacking alternatives that maintain relevance without data collection and storage on server devices.
Innovation Solution
Implementing advertisement auctions on client devices, where client devices analyze locally stored user data to select relevant advertisements without transmitting or storing user data on server devices, using server-derived digital asset vectors, predicted tap-through rates, and bid amounts to determine the most relevant digital assets for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If server devices collect, store, and analyze user data to deliver relevant advertisements, then advertisement relevance is improved, but user privacy is compromised
Solution Approach 1:
Instead of having servers collect user data to determine advertisement relevance, the patent inverts the approach by having client devices perform the analysis locally. The server provides only advertisement candidates and their feature vectors, while the client device compares these against locally stored user profiles to determine relevance, thereby preserving user privacy while maintaining advertisement effectiveness.
Solution Approach 2:
The patent extracts the data analysis function from the server and relocates it to the client device. By taking out the user data from the server environment and keeping it localized on client devices, the system eliminates the privacy risk associated with centralized data collection while preserving the capability to perform relevance analysis.
2Adaptability or versatility
If user data is transmitted to and stored on server devices for advertisement analysis, then advertisement personalization is improved, but data security risks increase
Solution Approach 1:
The client device performs the advertisement relevance analysis itself using locally stored user data and advertisement feature vectors provided by the server. This self-service approach eliminates the need to transmit sensitive user data to servers, allowing the system to maintain high advertisement personalization while the client device independently handles the security of its own data.
3Loss of information
If conventional server-based advertisement systems are used, then comprehensive data analysis is achieved, but user control over personal information is lost
Solution Approach 1:
The patent inverts the traditional centralized analysis model by distributing the analysis capability to client devices. Users maintain control over their personal information because the data never leaves their devices, while the system still achieves comprehensive analysis by comparing locally stored user profiles against advertisement feature vectors provided by the server.
Data Source
AI summary
Embodiments set forth techniques for managing advertisement auctions on a client device. The method can include the steps of (1) receiving, from a server device, a plurality of objects, where each object is associated with a respective digital asset, and each object includes, in association with the respective digital asset (i) a server-derived digital asset vector, (ii) a server-derived predicted tap-through rate, and (iii) a bid amount. In turn, and for each object of the plurality of objects, the client device (2) generates a respective estimated cost per impression for the object based on the information provided by the server device as well as information derived by the client device. Subsequently, the client device (3) identifies, among the plurality of objects, the object associated with the highest respective estimated cost per impression, and (4) causes an advertisement for the respective digital asset associated with the identified object to be displayed.