Privacy Preserving Data Collection Using Probabilistic Structures

Solution Overview

Problem

Existing methods for collecting and analyzing user data to improve user experience often compromise user privacy, as they require storing raw data that is susceptible to unauthorized access and resource-intensive, leading to inefficiencies in content distribution.

Innovation Solution

Implementing a privacy-preserving data collection and analysis method using probabilistic data structures, such as bloom filters, that mask user data with hashed representations, reducing the risk of data leaks and resource utilization by allowing only encrypted, privacy-enhanced data to be stored and transmitted, with additional layers of protection through public and private key encryption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If raw user data is stored to enable content distribution optimization, then content distribution efficiency is improved, but user privacy is compromised and data security risk increases

Engineering Contradiction:

Improvecontent distribution efficiencyVSAvoiduser privacy compromise

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent creates a probabilistic data structure that is a compressed representation (copy) of user data, containing only hashed identifiers and metadata rather than raw data. This copy enables content distribution optimization while the original raw data remains protected, resolving the contradiction between distribution efficiency and privacy protection.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent extracts only the essential information needed for content distribution (hashed user identifiers, content category metadata) from the raw user data, separating this minimal necessary data from the full raw data. This extraction allows efficient content distribution while the remaining raw data can be securely deleted or anonymized, addressing both productivity and privacy concerns.

Inventive Principle:

Principle #2Taking out (Extraction)

2Adaptability or versatility

If raw user data is stored for analysis, then user experience improvement is enabled, but resource consumption increases and data security risk increases

Engineering Contradiction:

Improveuser experience personalizationVSAvoiddata storage volume

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent creates a probabilistic data structure that is a compressed representation (copy) of user data, containing only hashed identifiers and metadata rather than raw data. This copy enables content distribution optimization while the original raw data remains protected, resolving the contradiction between distribution efficiency and privacy protection.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent transforms user data from raw format to hashed format, changing the fundamental parameters of data representation. This transformation reduces data volume significantly while maintaining the ability to perform analysis and personalization, resolving the contradiction between adaptability and data quantity.

Inventive Principle:

Principle #35Parameter changes

3Object-affected harmful factors

If probabilistic data structures with hashing are used to mask user data, then privacy protection is improved, but data processing complexity increases

Engineering Contradiction:

Improvedata security risk reductionVSAvoiddata processing complexity

Core Design Contradiction:

Object-affected harmful factorsVSDevice complexity

Solution Approach 1:

The patent creates a probabilistic data structure that is a compressed representation (copy) of user data, containing only hashed identifiers and metadata rather than raw data. This copy enables content distribution optimization while the original raw data remains protected, resolving the contradiction between distribution efficiency and privacy protection.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent transforms user data from raw format to hashed format, changing the fundamental parameters of data representation. This transformation reduces data volume significantly while maintaining the ability to perform analysis and personalization, resolving the contradiction between adaptability and data quantity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240427931A1Privacy preserving data collection and analysis

Publication Date: 2024.12.26 GOOGLE LLC

AI summary

A method includes receiving, by a data processing apparatus and from a content distribution system, a message comprising a probabilistic data structure representing a set of content items that should not be provided to a user device, content item data for content items available to be provided, and a request to determine whether any content item data is invalid, determining that the content item data for a given content item is invalid because the given content item may be in the set of content items represented by the probabilistic data structure, including removing the content item data for the given content item that was determined to be invalid; and preventing distribution of content items including the given content item.