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(Keynote Talk) Unlocking the Power of Artificial Intelligence for Precision Medicine with Privacy-Enhancing Technologies
Prof. Jean Louis Raisaro, CHUV-UNIL, assistant professor of Biomedical Informatics and Data Science at the Faculty of Biology and Medicine and the head of the Clinical Data Science Group at the Biomedical Data Science Center
Wednesday, Oct. 11, 2023 · 1:22 p.m. · 27m 56s
Artificial intelligence (AI), and particularly machine learning (ML), has an opportunity to improve the practice of healthcare and overall health and welfare of patients. AI tailored to the medical domain has existed for over half of a century and there have been demonstrations of its potential. However, only recently has ML at scale has become achievable. This is due to a variety of factors including access to high performance computing environments, the collection of large amounts of patient information in clinical and biomedical research environments, and advances in ML methodology (notably deep learning) and its accessibility in easy-to-use software packages. Still, the development and implementation of AI within the everyday operations of healthcare is a complex lifecycle that contains many points of failure for safe, effective, and equitable transition of AI into actual clinical practice. Even in countries with highly advanced scientific infrastructures, there are range of ethical challenges, e.g., 1) appropriate sourcing and access to patient data, 2) ensuring algorithmic fairness, 3) achieving value-based regulatory approval for AI technologies, and 4) safe translation of such technologies into practice in a way that healthcare organizations can support and monitor. In this talk, we will discuss how advanced privacy-enhancing technologies (PETs) can address concerns related to access and sharing of patient dataset. Particularly, we will focus on how federated data science enhanced by secure computing offers an optimal solution to achieve very large scale, privacy-preserving, health data sharing in cross-institutional settings.
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hello everybody an uh thanks for having me thanks for the invitation
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um so i'm going to talk to a little bit
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about the uh privacy machine learning and uh and health care
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and uh and tell you a little bit about the work of many people across the the last uh i would say
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five years um so as you know like our after system
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is facing unprecedented challenges so we have a constantly gauging population
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uh the cost of health care is increasing without really
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uh any plateau on side so uh we're insurance uh
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will uh will increase as well as of next year apparently
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and we had to deal with the pandemic so so all of these uh uh seems uh
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diverging quite a bit right so um so something is is being well i mean i mean the whole communities
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be talking a lot about is is precision madison right so how can we make sense of all of this
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massive amount of data we we're generating in hospitals in in laboratories
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uh that can go from very that the the very tiny like the the genome uh
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up to transcript on a core uh the effect of the environment or also like uh
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uh i'm mobile devices a fit bates and and so on at two
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uh what we can collect in in hospitals uh which
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can be information that you can find in the electronic up
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record so we can be structured it can be as structured in form of tax it can signal it can be imaging
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so all of this data seems good but how to make sense of it
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um and essentially transition from the current approach we're uh uh you know you
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have one treatment fits all approach to so called the uh personalise magazine or
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prevented many seen what hopefully will be able to target that appears on uh
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a sub group of people and try to reduce cost
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at the same time um so of course say i uh
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comes into the picture right and and uh it brings a lot of promises on
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how to make sense of this data and how to use this data to make predictions
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um but uh expression in the after sector
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uh its implementation is hindered by by several challenges
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right so we had the the data sharing an privacy challenge so
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i will uh uh expand the little bit on that during the talk
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with of course the data quality and standardisation so in a data we have a nice people
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is very sparse so there's a lot of missing data
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it's uh open low quality so a very little late labels
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so it's it's hard to uh to use uh we have
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challenges about transparency uh of data like we're the data is coming
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from right what was the process of the leading edge of the
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data which was collected by whom uh what kind of intermediate process
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uh it underwent a and also of course of a i algorithm so uh the meat are from in
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for this morning mention about explain ability interpret that really sorry this is whole all another field of research
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uh but then there are questions also about a patient safety right we've seen in the ah the
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the different race categories so i'm pretty sure i'm
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a i'm after falls into the high risk category
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um so so and accountability right save the mistakes is made
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uh who is accountable is the yeah i developer is doctor uh is the one who collected the data in the first place
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so all of these are still open questions and uh and another
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important aspect is of course the interaction of a i with uh
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uh with the workforce right so how a i would change will be the clinical work flows and so on
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um so do then gonna focus really on on uh probably the very first problem we have to
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address right in in the medical medical field is getting access
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to data right so in the medical field is extremely difficult
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uh to get access to data and and the
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reality is that today uh even if there are like
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pants and maybe hundreds of papers coming out every
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year about predicted models and how we can actually
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uh do things in elk or uh the reality is that very few of this
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model would get into into the clinic right and can be used at the bedside
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and and this is because uh most of the small though are not valid
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right so the the usually stop at okay i can get good performance on my test set
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but rarely they try to to get into a validation another
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court evaluation a so called external validation rides and uh this publication
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but um of last year's show that essentially this is a trend and involving trent
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right more kind more a publication get out uh that very few very few about it
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and one of the reason is not the only one of course it is data sharing right so data showing is extremely difficult
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uh uh in the medical sector or we have technical challenges right that are about
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inter operability right so they uh that we collect for example in dos people geneva
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is he a um is different from in in the way we collect data
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in laws that so already sixty kilometres apart we cannot really share data easy
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right and then we have the the the fear of cyber security a cyber attacks and and all that stuff
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but it's also called the role challenge right so there's a there's a lot of this notion about data ownership
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so doctor who sees patient believes that the of these patients belongs to him
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that's not to write the data they lost the patient in the hospital is just the custodian
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but somehow this this is a quarter all uh um role block right that
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that prevents a data from flowing around a more easy and of course we've mentioned
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many times right regulation uh uh the federal like them data protection g. d. p. r.
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and so on and so forth i so how do people do right
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now right so the the the approach for data sharing his more trying to
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uh uh centralise they are in a single place rightward people then can get access to
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and this is known to be a a do part time of sending the data to the algorithm
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but of course from a security standpoint you have to trust uh the institution it is collecting this data to be
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a secure right to have a secure infrastructure uh to control
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who has access to the data and so on and uh uh
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this can be seen as a single point of fit right so it is it to shun
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seeing this example is compromised then all the data from institution a and b. is also compromise
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uh so of course this uh it implies a loss of control and one way of meeting
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getting races of course a tradition absurd any
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musician organisation techniques uh which have shown to be
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ineffective uh in many examples in the literature on the last uh ten to twenty years
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so in the end what happens is that people rely on a legal contract so we we put lawyers
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uh in the in the in the process and then of course it
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takes a lot of time is costly lawyers don't agree with each other
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uh and then uh research doesn't hop right so it's it's very slow
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so a new approach that was kind of uh uh introduced a few years ago by by google is
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really does a federated approach to data sharing work instead of sharing the data we're gonna sharing the ugly though
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uh so the advantages that is uh i would say is compliant it's it's private by design
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right we were not exposing data to direct a
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privacy leakage is because we're not sharing the data
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but we're just sharing go aggregated form of these data it can
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be like statistics or were machinery mall that we might have twenty
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uh so the advantages i said is that uh allows
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hospitals or data provider to keep control over did data um
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but it comes with the with some questions that i think they're not yet fully
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fully understood hopefully answer right so of course uh we've
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seen this morning that a machine learning model or not
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cannot be considered anonymous data so the question is is
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this federated approach really prizes preserving is it really worth it
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question number one question number two is that um
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do we need to trust institution c. or not right so because in the end institutions you will still get
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uh all the models or the partial model strain at each institution
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to to do the abrogation and uh what about it's it's legal qualification
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are we talking about on any musician or mm sudanese age uh so this is just
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an example for returning better but i guess this is not new for for many of you
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so it works as following so they're a model is train locally right at each institution and then uh this
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is shared with essential party that can be one of
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the stipulations or the cloud and then there's a aggregate shame
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uh that happen and uh the global model is then shared back
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uh for another a round of a round of training uh to do to
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the local institution and sign and so forth for a certain number of epochs
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and this allowed basically uh to to not sure data even though the and
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of course uh many people think that
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sharing statistics or local model is privacy preserving
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uh this might be the case when we have a lot of patience and not many attributes
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but in precision magazine we are more and more striking finding a patient
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populations word so we end up in a situation where we have but
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two page if your patience and a lot of attributes right and uh and uh and this
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poses pro so if we look in particular at at machine learning 'cause we were talking about a i
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uh one of the i think fundamental a problem is that in the end we can consider
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a machine learning model as a last c. compress version
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of the original data set right so it's still capture
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information about the original training set so um essentially a but
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we can exploit this knowledge right because the model will behave differently
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if it's exposed to the training data uh that was used to train the mobile
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um or if it is exposed to new d. n. uh essentially
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there's the whole community about the privacy in in machine learning is been
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exploiting this phenomenon uh to perform different type of the inference attacks right
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so we have the membership in france attack rate talked about this today
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so trying to infer if a given individual was part of the training set
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we have the attribute in france attacks where we try for example to infer attributes
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uh other partially known record uh in the training set
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with property in france and then we have great engine version
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or like the every construction right uh so of course a
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membership in france is the simplest one but it's it's probably
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if we can do a membership inference is very likely that you can do also the other at right
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and uh the the intuition behind that is that's a um samples that are in the training set
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uh so the the model will behave differently from samples that are in the training set
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uh with respect to sample that are not in the training set so in in ideally
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we would the model model to to general lies right when b. a. but the same way
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but in reality what happens is that models uh and more sophisticated models tend to over
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feet on the training data and then try to you you're gonna be able to to distinguish
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so as long as this there is this idea of generalisation gap right between
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um uh the loss of your uh integration function
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um on your training data and and and validation testing data
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your your always able to tune in for uh some some private information on the trains
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and uh this can be uh can be represented as a as a essentially
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uh trying to to reconstruct the the the last distribution on the training
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set versus the last distribution on the non training set and if you're able
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to really separate those two distributions a damn
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essentially you're able to carry out these uh these
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in france attacked his membership in france and ducks and how can be done this impact is
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essentially there are there are techniques called like a shot though model attacks
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where you can create a multiple models that at the same
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architecture of the one vector trying to attack um and then
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uh you can use this show the models to to rebuild uh the the the last distributions all
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the examples in the training set and in not in the training set and then train a second model
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which would be us uh essentially classifier that will try to distinguish
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the two the two distributions and then when you send me it like
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a new record to the to this attacker model it will tell you whether it belongs to the training set or not
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um and of course this can be pushed a pretty
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far and actually very recent papers said as shown that
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uh did i can be a reconstructed uh almost in an identical manner
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and uh it especially in federated learning were a data sets are smaller just because you want
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to put them together right so they they said that the different sites are smaller so models
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that are coming from the different hospitals are more susceptible to these kind of because fraction attacks
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so this works for a convolutional neural networks are
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also for a four g. b. t. like models
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and um and here for example it's it's it's an example where people
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uh from uh from uh uh technical university in in a new niche in germany
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a show that they could reconstruct some some enter i uh data with pretty pretty good good actors
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so of course all of this is to say that in the end even if we're sharing the model and not the data
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we're not really solving the problem right now so answering the questions we asked
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in the beginning is is federated approach really privacy preserving the answer is no
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what is the level of trust i think we have to try to this institution
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see uh as if he was seeing the the in the the the level data
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and uh we definitely cannot consider federated learning as an an any musician technique
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uh so sorry about that but the interesting thing is that the uh the privacy
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uh and security community have been developing for years a so called pricing is in technologies
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the dark technologies rooted on mathematical principle them to keep overlap your or statistics
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that can be used to compliment further to learning and try to me to get the pro
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so i'm gonna show you how essentially the combination all different technologies could be
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uh uh something really interesting and also as a
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one of the talk this morning mention the there's not technology that
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can solve all the prominence most are times a combination of different what's
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so the first approach thank you uh the first approach uh that
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that could kind to mind is using differential privacy so differential privacy
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it's a formal notion of privacy that essentially tells you uh that if you
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have two database that are different in just one record so x. n. x. brine
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and then you have an algorithm or an analysis am uh that the probability of the outcome uh
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it's been the the ratio of the probability of the outcome from the two data set is bounded by
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a a finite uh and number which is actually a
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and and and this provides very very strong guarantees so
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when we apply differential privacy to machine learning we have a version of us to cast
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a gradient descent uh that uses differential privacy what essentially you can add some noise during
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uh during the the the gradient descent algorithm
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and essentially a provides you with this uh privacy guarantees
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so one way of looking at a differential pricing federated learning is basically uh
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instead of sharing models uh in the clear
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text you with the use stochastic differentially private
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stochastic gradient descent when you train your model so introducing some noise in the model training um
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and you will have like some prove mathematical guarantees that you
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cannot perform these kind of inference attacks that that shouldn't be um
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of course the use of the noise comes with a with a huge toll on on big predation and also some other
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uh and desired effects like it it introduces some bias and some it increases
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arbitrary mess of production so it's probably not the best way of solving the problem
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so another technology it was discussed today it's it's really all market encryption so is this particular type
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of encryption that allows it to compute uh on on encrypted data and just that as an example
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uh as a toy example this became started to became popular
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uh when people wanted to just clout to compute uh on on a on sensitive data
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and then essentially the idea is that you can interrupt your your sensitive data set into the clouds
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and then for example the cloud would run some some inference or some
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a segmentation on on the image and then send it back uh to the hospital where the
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description tin can decrypt um so this this is more or less how home arctic encryption works
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and it works well for um to party computation right so you have the
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and the the person owning the data and the so the data controller and the data processor
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uh and so this this works pretty well in in in this way
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and there's been like many progress is in you know like breaking the boundaries and and
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will be the the conception of these being a very complex and and a resource intensive computation
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uh so now we are i think we're able to train simple machine learning model something pretty data
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um so this is going going uh uh going for pretty fast
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uh of course the problem comes when we have more than two parties right if you want to share data
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across more than two parties another interesting thing is secure multiparty computation so this is
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is not using on the market encryption but uses other
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other techniques always try to pretend happy were essentially the goal
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is to compute a function over secret uh input without revealing each other
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which is uh the input to the other part is one of the limitation
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of a secure multiparty competition updated cans with high communication overhead
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because there's a lot of back and forth of messaging across the different entities um
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so what we uh what we decided to use eventually was a combination of the two
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so combination of all the more pick encryption and secure people a multiparty computation that
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uh it's known as a a multiparty almost the contraption uh where the idea is that
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essentially you would use secure multiparty computation
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for every operation that involves a secret key
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um and you would use like a more pick
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encryption for performing operations in a in a outsourcing button
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so essentially what uh how how this would work is that you would uh you would create
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an encryption key that you would secret share with a secure multiparty computation algorithm to the different parties
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and then use the encryption key which is public to include the data
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and data can be then processed and they're all more thick encryption
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and then whenever you need some bootstrapping so to refresh your uh
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your encryption uh to to allow for more complex operation in the side uh is separate
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that's the main you can use a secure multiparty computation protocol which makes things much faster
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then all more fake encryption and so the idea that we that we had was using
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multiparty more pick encryption on top of federated learning what essentially
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the computation that happen within the institution security boundaries happens on the
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plain text in then we wouldn't creaked with multiparty more quick encryption the model
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so this would guarantee that essentially institutions you
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would not see anything but encrypted models we could
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still perform aggravation because it relies on the on the more the properties of the get the system
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and as a consequence inference techniques during the training process would
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not be possible anymore and then just use the french of privacy
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at the very right so so then essentially meaty
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gating the utility lost the two with the encounter
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and uh if you would use differential privacy it every step of the process um
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so of course we we we wanted to
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to test how does a approach uh could scale
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and uh we we try to reproduce some more to send you get the sense that it that
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uh carried out computation by centralise in data in a single place so we took the same data set
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we split it apart and then we tried so we try to survival
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analysis and with so that we could reproduce exactly the same results uh
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regardless of the number of data providers this was skating very fast so
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even with the whole most a hundred they uh providers we we could stay within
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uh within ten seconds and we also tried more intensive computation like do you know why the situations that is
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what you would basically train one model for for each of the positions
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uh uh in the genome and and this we show that
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we could do a essentially reproduce uh the same the same result
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so here on the left you at the the original course so this is a standard manhattan
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plotted were essentially dots that pass the the significance
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line are considered to be associated with the phenotype
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that you're standing in in this case was a viral load of h. again the plot
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and then you see that if you use this the same approach
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by distributing dating different places and running this under or more thinking friction
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you would get the same results which is much better that what people do nowadays which is
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made kinda like this where instead of training collaborative really a model on the data huge usher statistics
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and it would be much better then training data on just one single hospital data
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where it essentially you would see no see well and of course scalability uh it takes more than a than a
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survival are nice but it's still in the real all
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the practicality and this was pushed and many publication follow that
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uh and of that is recent one about uh mm sell classifications
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uh using convolutional neural networks uh with this approach and that does all technology uh
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i mean we we had pretty good the publications uh but i think it's something that it's it's not that
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frequent is uh the the transfer of the technology into
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some spinoff and the ability to to put this into
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i'm into concrete use cases so uh the lab in which
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i was part essentially a uh to give our created the spinoff
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uh we we are at should with a partner of the spinoff and in the squeeze person myself
00:25:05
network which is this initiative in switzerland trying to
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put together a and share data across university hospitals
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where a couple of use cases one imposition oncology where we build up or a whole softer system
00:25:18
that is able to provide the on call this in the molecule to more bored with the tool that allow
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them to for example compute survival curves in the real
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time across all patients in uh in the five university hospitals
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and we also implemented the same approaching laboratory madison where
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you can define for example the reference ranges for laboratory test
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that are more personalise to to didn't page and one question
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i'm almost close to the end is uh of course what is the legal qualification of this of this approach
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so we work with the legal and ethical expert
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from from a th uh everybody and in particular
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we're we we essentially an allies the uh um we're essentially we argue
00:26:06
that this technology can provide an any musician according to g. d. p. r.
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if we take a relative approach to g. d. p. r. um and we submitted
00:26:15
also this to the federal data protection authorities switzerland to uh but to have the feedback
00:26:21
and if it but was uh was clearly in the same
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direction that we uh alluded to in the paper uh so
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to conclude a um i think uh that as
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opposed to maybe other domains that are less regulated
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uh i'm the adoption of a i. m. l. in in after
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is liking due to several open challenges and of course out data sharing
00:26:46
uh is probably the first that we need to saul otherwise if
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we without they are we cannot be on any model and and that
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i think that in the community which on that privacy missing technologies
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couple with federated learning more further it analysis can be really instrumental
00:27:03
enabling large scale and a privacy preserving l. data sharing
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project uh um and with this kind of legal assessment i think you can really
00:27:15
boast a data sharing across jurisdictions and of course uh that we and i'm very proud
00:27:21
about this work that we did also with the start that because most of the time
00:27:26
as academics with the we stop at the paper uh but seeing these uh used by doctors
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um i think uh it's it's uh at least for me was that we
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we we we working so of course we don't want to stop to switzerland
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uh so we're talking also with uh with other colleagues in the us in germany and so
00:27:46
so the goal would be uh can i can we skip to hundreds of organisations with this approach
Conference Program
(Keynote Talk) Privacy-Preserving Machine Learning : theoretical and practical considerations
Prof. Slava Voloshynovskiy, University of Geneva, professor with the Department of Computer Science and head of the Stochastic Information Processing group
Oct. 11, 2023 · 7:55 a.m.
2509 views
5 minutes Q&A - Privacy-Preserving Machine Learning : theoretical and practical considerations
Prof. Slava Voloshynovskiy, University of Geneva, professor with the Department of Computer Science and head of the Stochastic Information Processing group
Oct. 11, 2023 · 8:40 a.m.
Enabling Digital Sovereignity With ML
Vladimir Vujovic, Senior Digital Innovation Manager, SICPA
Oct. 11, 2023 · 8:44 a.m.
5 minutes Q&A - Enabling Digital Sovereignity With ML
Vladimir Vujovic, Senior Digital Innovation Manager, SICPA
Oct. 11, 2023 · 8:58 a.m.
Privacy-Enhanced Computation in the Age of AI
Dr. Dimitar Jechev, Co-founder and CTO of Inpher
Oct. 11, 2023 · 9:01 a.m.
138 views
5 minutes Q&A - Privacy-Enhanced Computation in the Age of AI
Dr. Dimitar Jechev, Co-founder and CTO of Inpher
Oct. 11, 2023 · 9:20 a.m.
Privacy by Design Age Verification & Online Child Safety
Dr. Onur Yürüten, Head of Age Assurance Solutions and Senior ML Engineer in Privately
Oct. 11, 2023 · 9:26 a.m.
5 minutes Q&A - Privacy by Design Age Verification & Online Child Safety
Dr. Onur Yürüten, Head of Age Assurance Solutions and Senior ML Engineer in Privately
Oct. 11, 2023 · 9:41 a.m.
(Keynote Talk) Biometrics in the era of AI: From utopia to dystopia?
Dr. Catherine Jasserand, KU Leuven (Belgium), Marie Skłodowska-Curie fellow at Biometric Law Lab
Oct. 11, 2023 · 11:06 a.m.
5 minutes Q&A - Biometrics in the era of AI: From utopia to dystopia?
Dr. Catherine Jasserand, KU Leuven (Belgium), Marie Skłodowska-Curie fellow at Biometric Law Lab
Oct. 11, 2023 · 11:42 a.m.
AI and Privacy
Alexandre Jotterand, CIPP/E, CIPM, attorney-at-law, partner at id est avocats
Oct. 11, 2023 · 11:48 a.m.
5 minutes Q&A - AI and Privacy
Alexandre Jotterand, CIPP/E, CIPM, attorney-at-law, partner at id est avocats
Oct. 11, 2023 · 12:06 p.m.
Preliminary Pperspectives on the Ethical Implications of GenAI
Julien Pache, A Partner at Ethix and Venture Partner at Verve Ventures
Oct. 11, 2023 · 12:12 p.m.
5 minutes Q&A - Preliminary Pperspectives on the Ethical Implications of GenAI
Julien Pache, A Partner at Ethix and Venture Partner at Verve Ventures
Oct. 11, 2023 · 12:30 p.m.
AI & Media: Can You Still Trust Information
Mounir Krichane, Director of the EPFL Media Center
Oct. 11, 2023 · 12:32 p.m.
5 minutes Q&A - AI & Media: Can You Still Trust Information
Mounir Krichane, Director of the EPFL Media Center
Oct. 11, 2023 · 12:54 p.m.
(Keynote Talk) Unlocking the Power of Artificial Intelligence for Precision Medicine with Privacy-Enhancing Technologies
Prof. Jean Louis Raisaro, CHUV-UNIL, assistant professor of Biomedical Informatics and Data Science at the Faculty of Biology and Medicine and the head of the Clinical Data Science Group at the Biomedical Data Science Center
Oct. 11, 2023 · 1:22 p.m.
5 minutes Q&A - Unlocking the Power of Artificial Intelligence for Precision Medicine with Privacy-Enhancing Technologies
Prof. Jean Louis Raisaro, CHUV-UNIL, assistant professor of Biomedical Informatics and Data Science at the Faculty of Biology and Medicine and the head of the Clinical Data Science Group at the Biomedical Data Science Center
Oct. 11, 2023 · 1:50 p.m.
Genomics, AI and Privacy
Julien Duc, Co-Founder and Co-CEO of Nexco Analytics
Oct. 11, 2023 · 2:01 p.m.
5 minutes Q&A - Genomics, AI and Privacy
Julien Duc, Co-Founder and Co-CEO of Nexco Analytics
Oct. 11, 2023 · 2:18 p.m.
How trust & transparency lead the success of an Idiap student's Master's project in fraud detection
Raphaël Lüthi, Machine Learning and AI Lead at Groupe Mutuel
Oct. 11, 2023 · 2:22 p.m.
5 minutes Q&A - How trust & transparency lead the success of an Idiap student's Master's project in fraud detection
Raphaël Lüthi, Machine Learning and AI Lead at Groupe Mutuel
Oct. 11, 2023 · 2:38 p.m.
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