Secure Cloud Enclave Privacy Segmentation for Personalized AI
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Solution Overview
Problem
The challenge lies in balancing the advancements of Artificial Intelligence (AI) with the imperative to safeguard individual privacy, particularly in secure cloud-based enclaves, where user data is stored, accessed, and trained, without compromising privacy.
Innovation Solution
A system and method that classifies user data into categories, applies data transformations such as anonymization, random numeric mapping, and time hashing, and uses AI agents to perform actions without exposing personal or secret information, while employing techniques like homomorphic encryption and Secure Multi-Party Computation (SMPC) to ensure privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user data is stored and processed in cloud-based enclaves to enable AI training and operations, then AI capabilities and personalization are improved, but user privacy and data security are compromised
Solution Approach 1:
The patent segments data into different classification levels (public, internal, confidential, restricted) and applies different processing and access controls to each segment. This allows AI training to utilize available data while protecting sensitive information through selective disclosure and access restrictions.
Solution Approach 2:
The patent introduces privacy-preserving technologies as intermediaries between data and AI processing, including federated learning frameworks that enable model training without centralizing data, differential privacy mechanisms that add noise to protect individual records, and secure enclaves that isolate sensitive data from external access.
2Object-affected harmful factors
If data transformations such as anonymization and random numeric mapping are applied to protect privacy, then user privacy is improved, but data utility and AI training effectiveness are reduced
Solution Approach 1:
The patent applies parameter changes through differential privacy mechanisms that adjust the level of noise added to data based on sensitivity requirements. This allows fine-tuned balance between privacy protection and data utility by controlling the privacy budget and noise intensity.
Solution Approach 2:
The patent applies different data transformation techniques to different data segments based on their sensitivity and utility requirements. High-sensitivity data receives strong anonymization while less sensitive data maintains higher utility for training, optimizing the balance locally across the dataset.
3Productivity
If collaborative AI training is enabled across multiple organizations, then AI model effectiveness is improved, but data security and trust between participants are compromised
Solution Approach 1:
The patent merges multiple data sources and training computations into a unified collaborative framework where distributed models aggregate insights while maintaining individual data sovereignty. This enables pooled intelligence without compromising individual organizational security or data control.
Solution Approach 2:
The patent introduces secure multi-party computation and trusted execution environments as intermediaries that enable collaborative training while preserving data security. These technologies allow computations on encrypted data without exposing raw information to participants or external systems.
4Adaptability or versatility
If long-term user profiling and historical tracking are performed to improve personalization, then user customization is improved, but user privacy and trust are reduced
Solution Approach 1:
The patent implements periodic data retention policies and time-limited profiling where user data is processed and stored only for specific durations necessary for personalization. After the retention period expires, data is automatically deleted or anonymized, preventing long-term accumulation while maintaining short-term personalization effectiveness.
Data Source
AI summary
System and method for preventing breach of user privacy in a secure cloud-based enclave. The method comprises receiving, by a data acquisition module associated with the secure cloud-based enclave, user data from external sources. The data acquisition module classifies the user data into multiple categories, such as general information, personal information, and secret information. The data acquisition module applies data transformations to the user data based on the multiple categories to generate transformed data. Further, a training module associated with the secure cloud-based enclave, trains user-specific Artificial Intelligence (AI) models based on the transformed data. Furthermore, an AI agent associated with the secure cloud-based enclave executes the user-specific AI models to perform an action associated with the user data and provides a result of the action to an external system through an external interface.