International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Identity Graphs and Privacy-Safe Data Collaboration: A Guide to Modern Data Clean Rooms Hrishikesh Desai ---------------------------------------------------------------------***--------------------------------------------------------------------3. Addresses are validated against postal databases. Abstract - This paper presents a product-focused analysis of modern data collaboration platforms, with particular emphasis on identity resolution and secure data sharing capabilities. We examine how organizations can leverage identity graphs to connect disparate customer data sets while maintaining privacy compliance. The paper explores the transformation of personally identifiable information (PII) into anonymous identifiers, enabling secure data sharing in cloud environments. Our analysis covers practical implementation considerations, use cases, and the statistical methodologies that ensure data utility while protecting consumer privacy. The paper is written for product managers and business stakeholders who need to understand the technical concepts.
Deterministic vs. Probabilistic Matching Deterministic Matching relies on exact identifier matches (e.g., same email across datasets). This method offers high precision but lower match rates (~20-40%). Probabilistic Matching uses statistical models and AI to link records based on similarity scores, even when identifiers partially match (e.g., name spelling variations, different email formats). This approach increases match rates (4070%) but requires confidence scoring to minimize false positives. Hybrid Approaches combine both methods, using deterministic matching for high-confidence links and probabilistic models to increase coverage where direct matches are unavailable.
Key Words: Identity Resolution, Privacy-Preserving technology, Data Clean Rooms, Anonymous Identifiers, Secure Data Sharing, Differential Privacy, Multi-Party Computation, Privacy-Safe Identity Graphs
Identity Graphs & Persistent Identifiers
1.INTRODUCTION
Once linked, customer identities are stored as persistent, privacy-safe IDs, enabling secure data collaboration without exposing raw PII. Identity graphs allow businesses to map relationships between customer interactions, even when they occur across different devices, platforms, or data partners. Graph-based AI algorithms continuously refine identity resolution accuracy based on user behavior and new data signals.
In today's data-driven business environment, organizations need to share and analyze customer data across partners while maintaining strict privacy controls. This paper examines how identity graphs and cloud-based data clean rooms make this possible, focusing on practical applications and business value rather than underlying technical implementations. 1.1 Identity Resolution Fundamentals
Table 1: Identity Resolution Methods Comparison
Identity resolution serves as the backbone of modern data collaboration platforms, allowing organizations to merge fragmented customer profiles across different touchpoints while ensuring privacy and compliance.
Identity Resolution Methods Comparison
Key Components of Identity Resolution Data Ingestion & Standardization Raw customer identifiers (e.g., email addresses, phone numbers, device IDs) are collected from various sources such as CRM systems, websites, mobile apps, and offline transactions. Normalization processes ensure consistent data formatting: 1.
Emails are lowercased and trimmed.
2.
Phone numbers are reformatted to international standards.
© 2025, IRJET
|
Impact Factor value: 8.315
|
Aspect
Deterministic
Probabilistic
Match Confidence
100%
Right
Match Rate
Lower (20-40%)
Higher 70%)
Use Cases
Financial, Healthcare
Marketing, Analytics
Data Requirements
Exact Matches
Partial Information
Processing Speed
Faster
Identifier
ISO 9001:2008 Certified Journal
(40-
More computeintensive
|
Page 390