Searchable Encryption for Outsourced Data Analytics
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Solution Overview
Problem
Existing data analytics systems face challenges in ensuring the privacy and confidentiality of data and query methods when outsourcing data analytics, as both the data owner and the data analyst need to protect sensitive information while enabling effective querying.
Innovation Solution
A searchable encryption system that allows data owners to encrypt their data once and communicate it in encrypted form to analysts, enabling keyword searches and range queries while keeping the data and query methods private, using query tokens that reuse previous results without direct interaction, and is provably secure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is encrypted to protect privacy, then data confidentiality is improved, but query capability deteriorates
Solution Approach 1:
The patent introduces query tokens as an intermediary mechanism that enables searching encrypted data without revealing either the data or the query to the other party. The data analyst generates query tokens based on encrypted data patterns, allowing searches on ciphertext while maintaining privacy for both sides.
Solution Approach 2:
The search operation is segmented into two independent parts: data encryption by the data owner and query token generation by the data analyst. This segmentation allows each party to operate on their own data without revealing sensitive information to the other, resolving the contradiction between encryption and query capability.
2Productivity
If data is made available to third parties for analysis, then data accessibility is improved, but data privacy deteriorates
Solution Approach 1:
The searchable encryption system acts as an intermediary that enables data sharing while preserving privacy. The data owner encrypts data and shares ciphertext with the data analyst, who can perform queries without accessing the actual data, thus maintaining both accessibility and privacy.
Solution Approach 2:
The system creates a copy of the data in encrypted form that can be searched and analyzed without revealing the original data. The query tokens operate on this encrypted copy, allowing analysis while the original data remains private to the data owner.
3Reliability
If query methods are kept private to protect intellectual property, then query security is improved, but data analysis effectiveness deteriorates
Solution Approach 1:
Query tokens serve as an intermediary that allows the data analyst to perform effective searches without revealing their query methods to the data owner. The tokens are generated locally by the analyst based on their analysis needs, enabling effective querying while maintaining query privacy.
Solution Approach 2:
The data analyst can generate query tokens independently without needing to disclose their analysis methods or receive assistance from the data owner. The system enables self-service querying where the analyst operates autonomously on encrypted data using their own query strategies.
Data Source
AI summary
A method for performing data analytics on outsourced data may include receiving, at a data analyst, cipher text representing data from a data owner such that the data remains hidden from the data analyst, generating a query token using a constant provided by the data analyst such that the constant remains hidden from the data owner, and analyzing the cipher text using the query token.