Deep learning and machine learning more broadly depend on large quantities of data to develop accurate predictive models. In areas such as medical research, sharing data among institutions can lead to even greater value. However, data often includes personally identifiable information that we may not want to (or even be legally allowed to) share with others. Traditional anonymization techniques only help to a degree. In this talk, Red Hat's Gordon Haff will share with you the active activity taking place in academia, open source communities, and elsewhere into techniques such as differential privacy and secure multi-party computation. The goal of this research and ongoing work is to help individuals and organizations work collaboratively while preserving the anonymity of individual data points.

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Preserving privacy
while sharing data
Gordon Haff
Emerging Technology Evangelist
@ghaff
https://bitmason.blogspot.com
September 2020

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Shared data can
accelerate innovation
and improve outcomes
in energy, telecoms,
healthcare...
The opportunity

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But data can be private
and sensitive at the
individual person or
organization level
The problem

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Source: Andrew Trask

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Anonymization
Removing/tokenizing personal
data fields
Encrypt/transform personal
data fields
Aggregation by trusted agency

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Does it work?
Sort of...

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What’s personal data?
Who can you really trust?
Lack of data diversity (e.g.
k-anonymity failures)
Susceptibility to attack

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Reconstruction
Source: US Census

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Identification of patterns
Source: https://avtanski.net/

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Source: Privitar
Linkage attacks

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Re-identification

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US Census

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US Census

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Differential Privacy
Response to erosion of traditional Statistical
Disclosure Limitation (SDL) techniques
Widely share statistics over a set of data without
revealing anything about individuals
2006 Dwork, McSherry, Nissim, and Smith
(ε-differential privacy)

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Requirements
Formal model
Resist linkage attacks
Resist unknown future attacks
Effective in settings in which
extensive external information
may be available

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Injects random data into a data
set (in a mathematically rigorous
way) to protect individual privacy
Value of randomness trades off
privacy and utility/accuracy
https://www.accessnow.org/understanding-
differential-privacy-matters-digital-rights/

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Limitations
Base rate
Noise
Repeated queries

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But what if you don’t
have a trusted
third-party?

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Multi-Party Computation
Collaborative analysis of silo-ed datasets
without trusting a third party
● Equivalence to incorruptible trusted party
● Parties jointly compute a function on their
inputs using a protocol
● No information is revealed about inputs

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Preserve privacy and correctness
Adversarial participants
Collusion
Threat models
Overhead
Considerations

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Protocol distributes encrypted
(AES) shares of (masked) data
Implementations and efficiency
depend on threat assumptions
In general, low compute but high
communications overhead

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Ongoing research
● Subscribe to:
https://research.redhat.com/quarterly/
● Boston University Red Hat Collaboratory
● openmined.org (PySyft)

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Thank you