Encrypted Impersonation Detection via Client-Side Yao Protocol
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Solution Overview
Problem
Conventional impersonation detection systems face liabilities and regulatory issues due to the need to store sensitive historical login attempt data in raw form, which can be mitigated by encrypting this data, but existing risk models cannot process encrypted information effectively.
Innovation Solution
Implementing an impersonation detection method where the client computer receives encrypted historical access request data and instructions to perform impersonation detection using Yao's protocol, allowing the client to compute a risk score without decrypting the data and maintaining the third party's proprietary analysis methodology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical access request data is stored in raw form, then risk model computation is efficient and accurate, but sensitive information is exposed to third parties creating liability and regulatory issues
Solution Approach 1:
The patent changes the state of the data from raw/decryptable to encrypted form. The historical access request data is stored and processed in encrypted form, changing the parameter of data accessibility while maintaining computational functionality through cryptographic protocols.
Solution Approach 2:
The patent introduces cryptographic protocols (specifically secure multiparty computation and homomorphic encryption) as intermediaries between the third-party risk model and the encrypted data. These intermediaries enable computation on encrypted data without exposing the underlying sensitive information, acting as a mediator that preserves both security and functionality.
2Object-affected harmful factors
If data is encrypted before transmission to third party, then information security is improved, but existing risk models cannot process the encrypted information
Solution Approach 1:
The patent transforms the computational paradigm by changing how the risk model operates - instead of requiring decrypted data input, the model is adapted to work with encrypted data through cryptographic protocols. This parameter change in the processing mode enables both security and model functionality.
Solution Approach 2:
The patent replaces the traditional mechanical data processing system (decrypt → process → encrypt) with a cryptographic system that processes data in encrypted form directly. This substitution uses mathematical/crypto mechanisms instead of conventional data processing mechanisms to achieve the same risk assessment function while maintaining security.
3Object-affected harmful factors
If conventional encryption methods are used, then data security is improved, but computation speed decreases significantly compared to raw data processing
Solution Approach 1:
The patent uses partial decryption or selective decryption approaches where only specific portions of data are decrypted when absolutely necessary, or uses homomorphic encryption that allows certain operations to be performed on encrypted data with acceptable performance characteristics, rather than fully decrypting all data for processing.
Data Source
AI summary
Techniques of performing impersonation detection involve using encrypted access request data. Along these lines, an impersonation detection server stores historical access request data only in encrypted form and has no way to decrypt such data. When a new access request is received by a client, the client sends the username associated with the request to the server, which in turns sends the client the encrypted historical access request data. In addition, the server sends the client instructions to perform impersonation detection. The client then carries out the instructions based on the encrypted historical access request data and data contained in the new access request.