Encrypted Browser Profiles for Cookie-Free Online Fraud Malware Detection
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Solution Overview
Problem
Existing methods fail to effectively detect online fraud malware without using third-party cookies while maintaining client data security, particularly due to the reliance on IP addresses which violate privacy budgets and expose sensitive information.
Innovation Solution
A method utilizing a secure multiparty computation system to create models based on browser activity profiles, obfuscating IP addresses and user agents, and issuing trust tokens to identify fraudulent entities, thereby maintaining data security and enabling fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IP addresses are used to track clients across websites for fraud detection, then the ability to identify fraudulent browsers is improved, but client data security and privacy are compromised
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between fraud detection needs and privacy protection. This system uses secure multi-party computation to enable fraud detection algorithms to analyze browsing patterns and identify malicious publishers without directly exposing or transmitting sensitive client data such as IP addresses and user profiles. The intermediary architecture allows computation on encrypted data, thus resolving the contradiction between detection accuracy and data security.
Solution Approach 2:
The patent replaces traditional mechanical tracking methods (direct IP address logging and centralized data collection) with cryptographic mechanisms. Specifically, it employs secure multi-party computation protocols, homomorphic encryption, and zero-knowledge proofs to enable fraud detection without direct access to plaintext client identifiers. This substitution eliminates the need for mechanical data exposure while maintaining detection capabilities through mathematical proofs and encrypted computations.
2Measurement precision
If third-party cookies are used to collect browsing history across multiple websites, then the ability to detect publisher inflation is improved, but data security and user privacy are compromised
Solution Approach 1:
The patent replaces the mechanical third-party cookie system with cryptographic mechanisms. Instead of using cookies to store and transmit browsing history in plaintext, the system employs encrypted user profiles and secure multi-party computation. Browsing history is processed through cryptographic protocols that allow analysis of patterns without exposing raw data, thus eliminating the security vulnerabilities inherent in third-party cookie-based tracking while maintaining the ability to detect publisher inflation.
Solution Approach 2:
The patent introduces an intermediary cryptographic layer between the client browser and the fraud detection system. This intermediary process encrypts browsing data locally in the browser and performs secure computations without requiring centralized cookie storage. The intermediary architecture enables cross-site browsing history analysis through encrypted channels, replacing the direct cookie-based mechanism with a security-enhanced alternative that maintains tracking accuracy while protecting user privacy.
3Adaptability or versatility
If client data is collected and analyzed centrally for fraud detection, then the comprehensiveness of fraud detection is improved, but data security and privacy protection are worsened
Solution Approach 1:
The patent segments the centralized data collection architecture into distributed client-side and server-side components. User profiles and browsing data are generated and encrypted locally in the browser (client-side segmentation), then processed through secure multi-party computation protocols (distributed computation segmentation). This segmentation eliminates the need for a centralized data repository, allowing comprehensive fraud detection through distributed analysis while reducing data security risks by keeping sensitive information localized and encrypted throughout the processing pipeline.
Data Source
AI summary
A method for secure detection of online fraud. The method includes generating an encrypted profile representing browser activity, sending the encrypted profile to a secure multiparty computation system, receiving a trust token from the secure multiparty computation system, based on a determination that the web browser is not engaged in online fraud, sending a request to redeem the trust token with the secure multiparty computation system, receiving an encrypted record of redemption from the secure multiparty computation system based on a determination that a web site associated with the web content is not blocked, and sending a request, containing the encrypted record of redemption, for third-party content, wherein the third-party content is associated with the web content.