Lightning Talk: Using Data without Compromising Privacy

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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 some degree. In this talk, Red Hat's Gordon Haff will share with you the active research activity taking place in academia and elsewhere into techniques such as multi-party computation and homomorphic encryption. The goal of this research is to enable broad information sharing leading to better models while preserving the anonymity of individual data points.

Technology

@ghaff https://bitmason.blogspot.com
2
Zombies : Brains
Machine Learning : Data

@ghaff https://bitmason.blogspot.com
3
Open data can further
accelerate the amount of
available data in energy,
telecoms, etc.

@ghaff https://bitmason.blogspot.com
4
But data can be private and
sensitive at the individual
person or organization level

@ghaff https://bitmason.blogspot.com
5
“Traditional” anonymization?
Removing personal data ﬁelds (e.g. names)
Aggregation by “trusted” agency

@ghaff https://bitmason.blogspot.com
6
Differential Privacy
Multi-Party Computation
Homomorphic Encryption

@ghaff https://bitmason.blogspot.com
7
Differential Privacy
Widely share statistics over a set
of data without revealing anything
about individuals who may be in
data
Injects random data into a data set
(in a mathematically rigorous way)
to protect individual privacy
https://www.accessnow.org/understanding-
differential-privacy-matters-digital-rights/

@ghaff https://bitmason.blogspot.com
8
Multi-Party Computation
“Sharing data without sharing it”
Allows collaborative analysis of
multiple silo-ed datasets without
trusting a central authority
Essentially replacing a trusted
third-party with a protocol by
distributing shares of secrets

@ghaff https://bitmason.blogspot.com
9
Homomorphic Encryption
Strongest variant is FHE (arbitrary functions
execute on encrypted data). Craig Gentry,
2009.
Lets a third party perform complicated
processing of data without being able to see
it
Lattice-based encryption schemes

@ghaff https://bitmason.blogspot.com
10
Further Reading
https://research.redhat.com/quarterly/

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Red Hat is the world’s leading provider of enterprise
open source software solutions. Award-winning support,
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Thank you

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Software projects were historically managed on a bet the farm model. They succeeded or they failed. And when they failed (as big software projects often did), the consequences were typically dire for, not only organizations as a whole, but for many of the individuals involved. Today, by contrast, many software and the development projects have evolved toward a much more incremental, iterative, and experimental process that takes cues from the open source model which excuses (and even rewards) certain types of failure. In this session, we’ll discuss how failure can be turned into a positive. This includes the organizational dynamics associated with tolerating uncertain outcomes, the need to define acceptable failure parameters, and the technical means by which experimentation can be automated in ways that amplify the positive while minimizing the effect of negative outcomes.

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The now mainstream platform changes stemming from the first Internet boom brought many changes but didn’t really change the basic relationship between servers and the applications running on them. In fact, that was sort of the point. Today’s workloads require a new model and a new platform for development and execution. The platform must handle a wide range of recent developments, including containers and Docker, distributed resource management, and DevOps tool chains and processes. The resulting infrastructure and management framework must be optimized for distributed and scalable applications, take advantage of innovation stemming from a wide variety of open source projects, span hybrid environments, and be adaptable to equally fundamental changes happening in hardware and elsewhere in the stack.

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The platform for developing and running modern workloads has changed. This new platform brings together the open source innovation being driven in containers and container packaging, in distributed resource management and orchestration, and in DevOps toolchains and processes to deploy infrastructure and management optimized for the new class of distributed application that is becoming the norm. In this session, Red Hat's Gordon Haff discusses the key trends coming together to change IT infrastructure and the applications that will run on it. These include: Container-based platforms designed for modern application development and deployment The ability to design microservices-based applications using modular and reusable parts The orchestration of distributed components Data integration with mobile and Internet-of-Things services Iterative development, testing, and deployment using Platform-as-a-Service and integrated continuous delivery systems

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Lightning Talk: Using Data without Compromising Privacy

1. 1 Using Data Without Compromising Privacy Gordon Haff Emerging Technology Evangelist @ghaff https://bitmason.blogspot.co m

2. @ghaff https://bitmason.blogspot.com 2 Zombies : Brains Machine Learning : Data

3. @ghaff https://bitmason.blogspot.com 3 Open data can further accelerate the amount of available data in energy, telecoms, etc.

4. @ghaff https://bitmason.blogspot.com 4 But data can be private and sensitive at the individual person or organization level

5. @ghaff https://bitmason.blogspot.com 5 “Traditional” anonymization? Removing personal data ﬁelds (e.g. names) Aggregation by “trusted” agency

6. @ghaff https://bitmason.blogspot.com 6 Differential Privacy Multi-Party Computation Homomorphic Encryption

7. @ghaff https://bitmason.blogspot.com 7 Differential Privacy Widely share statistics over a set of data without revealing anything about individuals who may be in data Injects random data into a data set (in a mathematically rigorous way) to protect individual privacy https://www.accessnow.org/understanding- differential-privacy-matters-digital-rights/

8. @ghaff https://bitmason.blogspot.com 8 Multi-Party Computation “Sharing data without sharing it” Allows collaborative analysis of multiple silo-ed datasets without trusting a central authority Essentially replacing a trusted third-party with a protocol by distributing shares of secrets

9. @ghaff https://bitmason.blogspot.com 9 Homomorphic Encryption Strongest variant is FHE (arbitrary functions execute on encrypted data). Craig Gentry, 2009. Lets a third party perform complicated processing of data without being able to see it Lattice-based encryption schemes

10. @ghaff https://bitmason.blogspot.com 10 Further Reading https://research.redhat.com/quarterly/

11. CONFIDENTIAL Designator linkedin.com/company/red-hat youtube.com/user/RedHatVideos facebook.com/redhatinc twitter.com/RedHat 11 Red Hat is the world’s leading provider of enterprise open source software solutions. Award-winning support, training, and consulting services make Red Hat a trusted adviser to the Fortune 500. Thank you

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