Microservice Data Anonymization for Secure Multi-Tenant Analysis
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Solution Overview
Problem
In multitenant cloud applications, the challenge lies in performing secure data analysis while protecting sensitive information, as existing solutions often compromise data privacy and application performance when sharing data across multiple users.
Innovation Solution
A microservice architecture is implemented, where tenant data is anonymized and stored separately, allowing for secure data analysis by isolating sensitive information and enabling secure data sharing without exposing identifying details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data of multiple customers is collected and analyzed together, then data analysis quality is improved, but data privacy is compromised
Solution Approach 1:
The patent extracts identifying information from customer data before analysis. A microservice removes personally identifiable information (PII) such as names, addresses, and contact details from the dataset, retaining only anonymized identifiers and analytical attributes. This allows multiple customers' data to be analyzed together while protecting their privacy.
Solution Approach 2:
The patent introduces an intermediary anonymization layer between data collection and analysis. An anonymization microservice acts as a mediator that transforms raw customer data into anonymized data structures, enabling analysis across multiple tenants without direct exposure of sensitive information. The intermediary preserves data utility while eliminating privacy risks.
2Object-affected harmful factors
If data is stored in dedicated database schemas for each customer, then data privacy is protected, but application performance deteriorates
Solution Approach 1:
The patent segments the system into separate microservices with distinct responsibilities: a data collection microservice, an anonymization microservice, and an analysis microservice. Each microservice operates independently on its own data structures, avoiding the performance penalty of complex multi-tenant database schemas while maintaining privacy through architectural isolation.
Solution Approach 2:
The patent changes the state of customer data from identified to anonymized through systematic parameter transformation. Identifying parameters (names, addresses, phone numbers) are removed or replaced with anonymized identifiers, fundamentally changing the data structure to enable both privacy protection and efficient shared analysis across multiple tenants.
3Adaptability or versatility
If identifying information is retained in shared data structures, then data utility is improved, but security is worsened
Solution Approach 1:
The patent creates anonymized copies of customer data for analysis purposes while retaining the original identified data in secure, isolated storage. The anonymization microservice generates copies with identifying information removed, allowing versatile analysis across multiple tenants without compromising the security of the original data. The copies maintain analytical utility while eliminating security risks.
Data Source
AI summary
According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.