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PPML JGI

PPML JGI

**Privacy-Preserving Machine Learning**: Machine learning on data you _cannot see_.

Privacy guarantees are one of the most crucial requirements when it comes to analyse sensitive information. However, data anonymisation techniques alone do not always provide complete privacy protection; moreover Machine Learning (ML) models could also be exploited to _leak_ sensitive data when _attacked_ and no counter-measure is put in place.

*Privacy-preserving machine learning* (PPML) methods hold the promise to overcome all those issues, allowing to train machine learning models with full privacy guarantees.

This workshop will be mainly organised in **two parts**. In the first part, we will explore one example of ML model exploitation (i.e. _inference attack_ ) to reconstruct original data from a trained model, and we will then see how **differential privacy** can help us protecting the privacy of our model, with _minimum disruption_ to the original pipeline. In the second part of the workshop, we will examine a more complicated ML scenario to train Deep learning networks on encrypted data, with specialised _distributed federated_ _learning_ strategies.

Valerio Maggio

June 15, 2022
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  1. Privacy Preserving


    Machine Learning
    Machine Learning on Data you’re not allowed to see
    [email protected]
    @leriomaggio
    github.com/leriomaggio/ppml-tutorial
    speakerdeck.com/leriomaggio/ppml-jgi

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  2. Provide an overview of the emerging tools (in the ecosystem)
    

    for Privacy Enhancing Technologies (a.k.a. PETs) with focus on
    

    Machine Learning
    Aim of this Tutorial
    Privacy-Preserving Machine Learning (PPML)

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  3. • Privacy-Preserving Machine Learning (PPML)
    technologies have the huge potential to be the
    

    Data Science paradigm of the future


    • Joint e
    ff
    ort of Open Source & ML & Security
    Communities


    • I wish to disseminate the knowledge about these new
    methods and technologies among researchers


    • Focus on Reproducibility of PPML work
    fl
    ows
    SSI
    Fellowship
    Plans :
    PPML
    What I would like to do
    How I would like to do
    gather.town
    Any help or suggestions about Use/Data cases or more
    generally Case studies, or any contribution to shape the
    repository will be very much appreciated!
    Looking forward to collaborations and contributions ☺
    Aw
    a
    rded by JGI Seed-Corn Fundings 2021
    je
    a
    ngoldinginstitute.blogs.bristol.
    a
    c.uk/2021/01/07/seed-corn-funding-winner-
    a
    nnouncement/

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  4. PPML Tutorial
    - Approach: Data Scientist

    -Always predilige dev/practical aspects (tools & sw)


    -Work on the full pipeline
    

    - Perspective: Researcher


    - References and Further Readings to know more


    - Live Coding 🧑💻 (wish me luck! 🤞 )


    - non-live coding bits will have exercises to play with.
    github.com/leriomaggio/ppml-tutorial
    Let’s switch to code to check that we’re
    all ready to start

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  5. Warm up
    DL Basics & PyTorch Quick Refresher

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  6. Deep Learning Terms
    Everyone on the same page?

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  7. also ref: bit.ly/nvidia-dl-glossary
    Epochs
    Batches and
    

    mini-batch learning
    Parameters vs HyperParameters


    (e.g. weights vs layers)
    Loss & Optimiser


    (e.g. Cross Entropy & SGD)
    Transfer learning
    Gradient & Backward Propagation
    Tensor
    Deep Learning Terms Everyone on the same page?
    Generative Adversarial
    Networks (GAN)

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  8. Python has its say
    Machine Learning
    Deep Learning
    “There should be one, and preferably one, way to do it”
    The Zen of Python

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  9. Multiple Frameworks?
    Data APIs: Standardization of N-dimensional arrays and dataframes, by Stephannie Jimenez Gacha
    

    https://2022.pycon.de/program/BMFVFG/

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  10. Main features overview
    review of basic PyTorch features we will see soon

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  11. Tensors, NumPy, Devices
    Numpy-like API tensor -> ndarray
    tensor <- ndarray
    CUDA support
    🙋
    torch.cuda

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  12. torch.nn
    

    Module
    subclassing
    De
    fi
    nition of layers
    (i.e. tensors)
    De
    fi
    nition of graph
    (i.e. network)
    🙋

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  13. Loss and Gradients
    optimiser
    criterion
    

    & loss
    backprop &
    update
    🙋
    torch.optim
    torch.nn

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  14. Dataset and DataLoader
    transformers
    Dataset
    DataLoader
    🙋
    torch.utils.data

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  15. Let’s Introduce Privacy

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  16. The Data Vs Privacy AI Dilemma
    AI models are data hungry:


    • The more the data, the better the model


    • Push for High-quality and Curated* Open Datasets
    * More on the Curated possible meanings in the next slides!
    High-sensitive data: we need to keep data safe
    from both intentional and accidental leakage
    Data &| Models are kept in silos!

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  17. The Data vs Privacy AI Dilemma
    AI models are data hungry:


    • The more the data, the better the model


    • Push for High-quality and Curated* Open Datasets
    * More on the Curated possible meanings in the next slide!
    High-sensitive data: we need to keep data safe
    from both intentional and accidental leakage
    Data &| Models are kept in silos! Data accounting for privacy
    

    (privacy preserving data)

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  18. Privacy-Preserving Data
    Data Anonymisation Techniques: e.g. k-anonimity


    • (From Wikipedia)
    

    In the context of k-anonymization problems, a database is a table with n rows and m columns.
    

    Each row of the table represents a record relating to a speci
    fi
    c member of a population and the
    entries in the various rows need not be unique. The values in the various columns are the
    values of attributes associated with the members of the population.
    D
    a
    t
    a
    Anonymity
    Dataset
    🔒K-Anonymised
    

    Dataset
    Algorithm #1
    Algorithm #2
    Algorithm #k
    Data Sharing
    https://github.com/leriomaggio/privacy-preserving-data-science

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  19. Privacy-Preserving Data
    Data Anonymisation Techniques: e.g. k-anonimity


    • (From Wikipedia)
    

    In the context of k-anonymization problems, a database is a table with n rows and m columns.
    

    Each row of the table represents a record relating to a speci
    fi
    c member of a population and the
    entries in the various rows need not be unique. The values in the various columns are the
    values of attributes associated with the members of the population.
    D
    a
    t
    a
    Anonymity Issues
    Source: https://venturebeat.com/2020/04/07/2020-census-data-may-not-be-as-anonymous-as-expected/
    […] (we) show how these methods can be used in practice to
    de-anonymize the Netflix Prize dataset, a 500,000-record
    public dataset.
    Linking Attack

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  20. Why don’t we allow AI without
    moving data from their silos?

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  21. Introducing: Federated Learning

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  22. So that’s it ?
    

    Federated Learning to rule them all ?

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  23. Model Vulnerabilities
    Adversarial Examples
    ppml-tutorial/1-fast-gradient-sign-method

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  24. Model Stealing
    Model Inversion Attacks
    ppml-tutorial/2-model-inversion-attack

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  25. Introducing
    Di
    ff
    erential Privacy
    Inspired from: Di
    ff
    erential Privacy on PyTorch | PyTorch Developer Day 2020
    

    youtu.be/l6
    f
    bl2CBnq0

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  26. View Slide

  27. Source:
    

    pinterest.com/
    agirlandaglobe/

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  28. View Slide

  29. View Slide

  30. View Slide

  31. PPML with Differential Privacy
    https://ppml-workshop.github.io
    Di
    ff
    erential privacy is a system for
    publicly sharing information about a
    dataset by describing the patterns of
    groups within the dataset while
    withholding information about
    individuals in the dataset.
    Like k-Anonymity, DP is a formal notion of privacy (i.e. it’s possible to prove
    that a data release has the property).
    

    Unlike k-Anonymity, however, di
    ff
    erential privacy is a property of algorithms,
    and not a property of data. That is, we can prove that an algorithm satis
    fi
    es
    di
    ff
    erential privacy; to show that a dataset satis
    fi
    es di
    ff
    erential privacy, we must
    show that the algorithm which produced it satis
    fi
    es di
    ff
    erential privacy.

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  32. Learning from Aggregates
    With Di
    ff
    erential Privacy
    • Aggregate Count on the Data


    • Computing Mean


    • (Complex) Train ML model
    Di
    ff
    erential Privacy within the ML Pipeline
    ppml-tutorial/3-di
    ff
    erential-privacy

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  33. Going back to: Federated Learning

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  34. Federated Learning
    

    & Encryption

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  35. Federated Learning
    

    & Homomorphic Encryption
    https://blog.openmined.org/ckks-homomorphic-encryption-pytorch-pysyft-seal/
    ppml-tutorial/4-federeted-learning

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  36. Thank you very much
    

    for your kind attention
    Valerio Maggio
    [email protected]
    @leriomaggio
    github.com/leriomaggio/ppml-tutorial
    speakerdeck.com/leriomaggio/ppml-jgi

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