Anonymizing User Data via Event Index Aggregation
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Solution Overview
Problem
Existing systems fail to provide anonymous feedback on user interactions, such as email readings or document access, without compromising user privacy, as users are hesitant to share this information due to privacy concerns.
Innovation Solution
A method and apparatus for anonymizing user data by aggregating event information across distributed systems, allowing anonymous aggregation commands to be executed without revealing individual user identities, using an event index that supports queries and set operations to generate anonymous user lists for actions like sending notifications or updating calendars.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user interaction data is collected and made available for feedback, then information utility is improved, but user privacy is compromised
Solution Approach 1:
The patent combines multiple user interaction records into aggregated statistics that preserve feedback utility while eliminating individual identifiability. Event indexes merge data from distributed systems into collective patterns that cannot be traced back to specific users, resolving the contradiction between information availability and privacy protection.
Solution Approach 2:
The event index acts as an intermediary layer between raw user interaction data and feedback systems. It transforms identifiable user actions into anonymous aggregated statistics, serving as a mediator that enables information flow while blocking privacy exposure. This intermediary structure allows feedback generation without direct access to individual user identities.
2Measurement precision
If individual user identities are revealed for feedback purposes, then feedback accuracy is improved, but user anonymity is lost
Solution Approach 1:
The system merges multiple user interaction events into aggregated statistics that maintain feedback accuracy through statistical representation while ensuring anonymity through aggregation. The event indexes combine data from multiple users and systems, creating reliable feedback metrics that cannot be attributed to individual users, thus resolving the contradiction between precision and anonymity.
3Quantity of substance
If distributed systems share user data for aggregation, then data completeness is improved, but system complexity increases
Solution Approach 1:
The event index structure serves multiple functions simultaneously: it stores raw event data, performs aggregation operations, enables cross-system queries, and generates anonymous feedback. This universal structure handles diverse data types and operations through a unified mechanism, reducing the complexity that would otherwise arise from separate specialized systems for each function.
Solution Approach 2:
The patent segments the distributed system into independent event indexes that can be maintained locally at different systems while being queryable across the network. Each system maintains its own event index separately, avoiding the complexity of centralized data storage while enabling complete data aggregation through coordinated queries. This segmentation allows data completeness without requiring complex centralized management.
Data Source
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AI summary
In a method of anonymising user data by aggregation, at least one server-side device (1) receives an anonymous aggregation command from a user client device (2). The anonymous aggregation command includes a specification of a set of users and an action to be taken. A list of users who meet the specification in the anonymous aggregation command is generated. The list of users who meet the specification is validated as meeting at least one criterion for anonymous aggregation. The action in the anonymous aggregation command is triggered to be taken in respect of the validated list of users who meet the specification.