Privacy-Preserving Entity Counting via Probabilistic Hash Collisions

Solution Overview

Problem

Existing methods for counting distinct entities in distributed systems, such as GUIDs, fail to adequately address privacy concerns as they can lead to entity tracking, and techniques like pseudonymization and differential privacy compromise the accuracy of entity counting.

Innovation Solution

A system that generates Entity Identifiers (EIDs) through a transformation function, allowing for collisions, which are then used to estimate the number of entities in a distributed system, providing enhanced privacy by making it impossible to determine the identity of entities with certainty, and employing approximation functions or algorithms like HyperLogLog for accurate counting.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If GUIDs are used to identify entities in distributed systems, then entity identification accuracy is improved, but privacy protection deteriorates

Engineering Contradiction:

Improveentity identification accuracyVSAvoidprivacy leakage

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent introduces EIDs as an intermediary between GUIDs and the entity counting system. EIDs are derived from GUIDs through a transformation function that allows collisions, thereby breaking the direct one-to-one mapping while still enabling entity identification through probabilistic methods. This intermediary layer protects privacy by making it impossible to deterministically identify entities from EIDs alone.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms the identification parameter from deterministic GUIDs to probabilistic EIDs by applying a transformation function that intentionally introduces collisions. This parameter change shifts the system from exact identification to approximate counting, achieving privacy protection while maintaining useful counting functionality through algorithms like HyperLogLog that are designed to work with colliding identifiers.

Inventive Principle:

Principle #35Parameter changes

2Object-affected harmful factors

If pseudonymization is applied to GUIDs to protect privacy, then privacy protection is improved, but entity counting accuracy deteriorates

Engineering Contradiction:

Improveprivacy leakageVSAvoidentity counting accuracy

Core Design Contradiction:

Object-affected harmful factorsVSMeasurement precision

Solution Approach 1:

The patent introduces EIDs as an intermediary between GUIDs and the entity counting system. EIDs are derived from GUIDs through a transformation function that allows collisions, thereby breaking the direct one-to-one mapping while still enabling entity identification through probabilistic methods. This intermediary layer protects privacy by making it impossible to deterministically identify entities from EIDs alone.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms the identification parameter from deterministic GUIDs to probabilistic EIDs by applying a transformation function that intentionally introduces collisions. This parameter change shifts the system from exact identification to approximate counting, achieving privacy protection while maintaining useful counting functionality through algorithms like HyperLogLog that are designed to work with colliding identifiers.

Inventive Principle:

Principle #35Parameter changes

3Object-affected harmful factors

If differential privacy is applied to increase privacy, then privacy protection is improved, but entity counting capability deteriorates

Engineering Contradiction:

Improveprivacy leakageVSAvoidentity counting capability

Core Design Contradiction:

Object-affected harmful factorsVSProductivity

Solution Approach 1:

The patent introduces EIDs as an intermediary between GUIDs and the entity counting system. EIDs are derived from GUIDs through a transformation function that allows collisions, thereby breaking the direct one-to-one mapping while still enabling entity identification through probabilistic methods. This intermediary layer protects privacy by making it impossible to deterministically identify entities from EIDs alone.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms the identification parameter from deterministic GUIDs to probabilistic EIDs by applying a transformation function that intentionally introduces collisions. This parameter change shifts the system from exact identification to approximate counting, achieving privacy protection while maintaining useful counting functionality through algorithms like HyperLogLog that are designed to work with colliding identifiers.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11368465B2Distributed entity counting with inherent privacy features

Publication Date: 2022.06.21 GEN DIGITAL INC

AI summary

Systems and methods provide an entity identifier (EID) for use in distributed systems, where the entity identifier includes inherent privacy features and where an estimate of the distinct count of the entity identifiers in a distributed system can be determined. A unique identifier (e.g., a GUID) for an entity is received. A hash value can be generated for the unique identifier using a hash function that is not guaranteed to generate unique values. An EID is created using a portion of the bits of the hash value and stored in a database. An estimated distinct count of entities based on a count of EIDs in the database can be determined based on the count of EIDs in the database and the size of the EID space.