Homomorphic Differential Privacy for Encrypted Database Queries

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Solution Overview

Problem

Database systems face challenges in performing statistical analysis on encrypted user data while maintaining data privacy, as existing encryption techniques do not support statistical analysis and decrypting data for analysis exposes sensitive information to potential security breaches.

Innovation Solution

Implementing homomorphic differential privacy mechanisms that transform statistical queries to inject noise into encrypted data without decryption, using homomorphic encryption to ensure that user data remains encrypted and private throughout the analysis process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If user data is encrypted at rest for data securitization, then data security and privacy are improved, but statistical analysis capability deteriorates because existing encryption techniques do not support statistical analysis

Engineering Contradiction:

Improvedata securityVSAvoidstatistical analysis capability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent introduces homomorphic encryption as an intermediary mechanism that allows statistical operations to be performed on encrypted data without decryption. The encryption scheme acts as a mediator between the encrypted storage requirement and the statistical analysis requirement, enabling both security and analytics capability to coexist.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the mathematical parameters of the encryption scheme to support homomorphic operations. By selecting encryption schemes with specific mathematical properties (supporting addition and multiplication operations on ciphertext), the system enables statistical analysis while maintaining encryption, thus resolving the contradiction between security and analytics capability.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If user data is decrypted for statistical analysis, then statistical analysis capability is improved, but data security deteriorates because a malicious user may access the data resulting in data leakage

Engineering Contradiction:

Improvestatistical analysis capabilityVSAvoiddata leakage risk

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

Homomorphic encryption serves as an intermediary that prevents direct exposure of plaintext data during statistical analysis. The encryption layer acts as a protective barrier that allows computations to proceed on encrypted data, eliminating the window of vulnerability where decrypted data could be accessed by malicious users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the traditional mechanical approach of decrypt-then-analyze with a cryptographic approach of analyze-then-encrypt. By substituting the order of operations and using homomorphic properties, the system achieves statistical analysis without the security risk associated with temporary decryption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If differential privacy mechanisms are applied to encrypted data, then user privacy is improved, but device complexity increases due to the need for query transformation and noise injection functions

Engineering Contradiction:

Improveuser privacy protectionVSAvoidquery processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements preliminary transformation of queries into the encrypted domain before execution. By pre-processing queries to include noisification functions and transforming them into the encrypted domain, the system prepares all necessary components for differential privacy protection in advance, reducing the complexity burden during actual data processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The query transformation layer acts as an intermediary that handles the complexity of differential privacy mechanisms. This intermediate layer manages query transformation, noise injection, and encryption operations, isolating the complexity from the core data processing operations and making the system more manageable.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11328082B2Differential privacy for encrypted data

Publication Date: 2022.05.10 KETCH KLOUD INC

AI summary

Methods, systems, and devices for data processing are described. Some database systems may support differential privacy for encrypted data. For example, a database may store user data as ciphertext. A system may receive a statistical query for the user data and may identify a relevant differential privacy mechanism. The system may transform the query to operate on encrypted data while including a noisification function based on the mechanism. The system may execute the transformed query at the database, involving adding noise to the query result according to the noisification function without decrypting the data. For example, the system may leverage homomorphic encryption techniques to inject the noise while the data remains encrypted. The database may return the noisified, encrypted query results, which the system may decrypt for statistical analysis. By applying differential privacy on the encrypted data, the system may avoid exposing any private user information throughout the process.