Camouflage Query System for Internet User Data Privacy Protection
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Solution Overview
Problem
Current methods for protecting user privacy on the internet, such as proxy servers and encryption, are inadequate as they do not effectively obscure user queries and patterns, leading to potential misuse of user data by servers and third parties.
Innovation Solution
A Camouflaging Query (CQ) system that generates computer-generated queries to obscure user intent, mimicking user queries and emulating user behavior, thereby making it difficult for servers to distinguish between intended and camouflage queries, thus protecting user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If proxy servers are used to protect user privacy, then user identity and communication content are partially obscured, but information in queries and responses between user and proxy server are still exposed to interceptors
Solution Approach 1:
The system creates a copy of the user's intended query and transmits it as a camouflage query without the user's knowledge. This copy serves to mislead interceptors and servers about the user's true intentions, while the actual query remains protected. The camouflage query is a replica that mimics the structure and format of genuine user queries.
Solution Approach 2:
The camouflage query acts as an intermediary between the user's true query and the server or interceptor. It provides a false layer of information that intermediates the communication, allowing the user's actual query to remain hidden while still enabling the system to function.
2Object-affected harmful factors
If encryption is used to protect user privacy, then communication content is secured, but the server still knows the history, content and pattern of user information
Solution Approach 1:
The system generates a camouflage query that is a copy or imitation of the user's intended query. This copy contains false information about user intentions and is transmitted to the server, thereby protecting the user's true query patterns and behavior from server analysis.
Solution Approach 2:
Instead of trying to hide the query directly, the system inverts the approach by actively sending false query information to the server. The camouflage query presents the opposite or misleading information about user intentions, thereby protecting the true query patterns through active deception rather than passive concealment.
3Ease of operation
If websites track user movements to provide personalized services, then user experience is improved, but user privacy is compromised as there is very little users can do about it
Solution Approach 1:
The system creates camouflage queries that copy the format and structure of genuine user queries but contain false information about user intentions. These copies are sent to websites to maintain the appearance of normal user activity while protecting the user's true movement patterns and preferences from tracking.
Solution Approach 2:
The system sends not only the user's genuine query but also additional camouflage queries that exceed the normal query volume. This excessive action of sending multiple queries (including false ones) confuses tracking systems and makes it difficult to accurately determine the user's true intentions and movement patterns.
Data Source
AI summary
In a query-response model system of network communication, to protect user data privacy, a plurality of Camouflage Queries are automatically generated to be sent along with Intended Queries. The mix of intended queries and the Camouflage Queries masks and obscures the intended queries. Camouflage Messages or queries create a noisy background and thus lower the signal to noise ratio (SNR) for a server or interceptor to detect the intended queries.