Private matching for compute: New solutions to the problem of enabling compute on private set intersections

Matching records is one of the most basic data analysis operations. There are many cases where data needs to be aligned across some common value — whether that’s joining between two different table…

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Prasad Buddhavarapu
•11 min read•intermediate•
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Rust

Overview

The article discusses new cryptographic protocols, Private-ID and PS3I, designed to enable private matching for compute in scenarios where data privacy is critical. It highlights the importance of privacy-enhancing technologies (PETs) in allowing parties to analyze shared data without exposing sensitive information.

What You'll Learn

1

How to implement Private-ID for secure data matching

2

Why privacy-enhancing technologies are essential for data analysis

3

How to use elliptic curve cryptography in private data exchanges

4

When to apply multiparty computation for complex data analysis

Prerequisites & Requirements

  • Understanding of cryptographic principles and private set intersection
  • Familiarity with Rust programming language(optional)

Key Questions Answered

What are the new cryptographic protocols for private matching discussed in the article?
The article discusses two new cryptographic protocols: Private-ID, which performs an outer join without revealing records, and PS3I, which performs an inner join and outputs encrypted matching records. Both protocols enhance privacy while allowing for data analysis.
How do Private-ID and PS3I ensure data privacy during matching?
Private-ID uses elliptic curve cryptography to encrypt records and only reveals the size of the intersection, while PS3I employs both elliptic curves and Paillier encryption to generate encrypted matching records as additive shares, ensuring that no sensitive information is disclosed.
What are the performance capabilities of the Private-ID and PS3I protocols?
The article states that Private-ID can generate IDs for 100 million records in 60 minutes, while PS3I can generate additive shares for 5 million records in the same timeframe, showcasing their efficiency in handling large datasets.
What are the applications of private set intersection in real-world scenarios?
Private set intersection can be applied in various scenarios such as calculating total donations across different categories, analyzing randomized control trials, and training machine learning models while keeping sensitive data private.

Key Statistics & Figures

Records processed by Private-ID
100 million
Private-ID can generate IDs for this number of records in 60 minutes.
Records processed by PS3I
5 million
PS3I can generate additive shares for this number of records in the same timeframe.

Technologies & Tools

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Programming Language
Rust
Used to implement the Private-ID and PS3I protocols.
Cryptography
Elliptic Curve Cryptography
Ensures the privacy of records during data exchanges.
Cryptography
Paillier Encryption
Used in PS3I for generating encrypted records as additive shares.

Key Actionable Insights

1
Implementing Private-ID can significantly enhance the privacy of data matching operations in your applications.
This is particularly useful in industries where data privacy is paramount, such as healthcare or finance, allowing organizations to collaborate without compromising sensitive information.
2
Utilizing PS3I can facilitate complex data analyses while ensuring that individual data records remain confidential.
This approach is beneficial for organizations looking to perform joint analyses without exposing their datasets, thus fostering trust and compliance with data protection regulations.
3
Adopting elliptic curve cryptography in your data exchanges can provide robust security against unauthorized access.
As data breaches become more common, implementing strong cryptographic measures is essential for safeguarding sensitive information during transmission.

Common Pitfalls

1
Failing to properly encrypt data before sharing can lead to privacy breaches.
It's crucial to ensure that all data is encrypted using robust cryptographic methods to prevent unauthorized access during the matching process.
2
Not considering the computational costs associated with private matching algorithms.
Understanding the performance implications of these algorithms is vital, especially when dealing with large datasets, to avoid inefficiencies in data processing.

Related Concepts

Cryptographic Protocols
Private Set Intersection
Multiparty Computation
Privacy-enhancing Technologies