Anonymized Communication via Noise Insertion for Privacy
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Solution Overview
Problem
There is a need for improved privacy management in anonymous communication systems, particularly for security-related information like indications of compromise (IOCs), where existing techniques lack efficient methods for anonymous feedback to a central entity without relying on complex cryptographic constructs.
Innovation Solution
A communication system where clients generate anonymized messages by inserting noise into security-related information and transmitting them to a server, allowing the server to extract characteristics without identifying individual clients, using noise values that cancel out, enabling anonymous feedback without ring signatures or similar cryptographic constructs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ring signatures or similar cryptographic constructs are used for anonymous communication, then privacy protection is improved, but device complexity and computational overhead increase
Solution Approach 1:
The patent uses disposable noise values that are generated, used once to anonymize a message, and then discarded. Each client generates a random noise value, adds it to their security information, and sends the anonymized message. The noise values are single-use and never reused, making the privacy protection mechanism simple and computationally efficient without requiring complex cryptographic infrastructure.
2Reliability
If noise values are inserted to anonymize messages, then individual client identification is prevented, but the ability to extract meaningful characteristics from aggregated messages must be maintained
Solution Approach 1:
The system implements a feedback mechanism where the central server aggregates anonymized messages from multiple clients, extracts statistical characteristics from the aggregated data, and can send feedback information back to clients. The server sums all received anonymized messages and uses the aggregated result to derive security insights while maintaining individual anonymity. This feedback loop ensures that meaningful information is preserved at the population level even though individual messages are anonymized.
Data Source
AI summary
A server is configured to communicate with a group of clients over a network. Each of the clients obtains a corresponding informational message comprising security-related information such as an indication of compromise (IOC), inserts noise in the information message to generate an anonymized message, and communicates the anonymized message to the server. The anonymized messages communicated by the respective clients to the server may be configured so as to prevent the server from identifying any individual client associated with a particular one of the anonymized messages, while also allowing the server to extract from the anonymized messages collectively one or more characteristics of the underlying informational messages. A given client may insert noise in an informational message by, for example, selecting a noise value from a specified range of noise values, and combining the informational message and the selected noise value to generate the anonymized message.