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Enabling Digital Sovereignity With ML
Vladimir Vujovic, Senior Digital Innovation Manager, SICPA
Wednesday, Oct. 11, 2023 · 8:44 a.m. · 13m 28s
Vladimir Vujovic, Senior Digital Innovation Manager, SICPA. Managing innovation in R&D Digital at SICPA with special focus in the field of authentic and verifiable data and decentralized identity. More than 10 years of experience in Product management in companies of different sizes, and more than 6 years of experience in the field of decentralized identity.
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um hello everyone so um my name is uh one which i'm i'm
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senior degeneration manager that's it but uh so today i'm
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gonna talk about some uh enabling distance tormented the machine
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also have to say i'm not really yes but in yeah and um my my the main uh oh expertise is
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but i have a financial then descends but then it but recently i haven't
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had a chance to actually collaborate on some projects that you're working with data
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huh so first to be on the talk about oh who we are
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as a company some of the challenges in the context of digital sovereignty
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uh some they use cases in facts uh into the mean of a a and machine
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learning and also some solutions that you're a docking inch and first we are quite sick but
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so sick days are leading provider of
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secure authentication invocation disability supply chain solutions
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we're mostly known for our um uh see
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you eating stand for security uh physical security features
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did you can find in in many um uh banknotes around the world
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uh we also known for our uh solutions for physical marking go products an excise goods
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um do to nature business you are uh along trusted adviser to garment central banks and
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and not putting pins and so on and we like to save ourselves that we are
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in the business of enabling trust to to our to our customers into our pockets
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no no we're also a global companies your your roughly three
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thousand people again and in many uh countries in the world
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v. provides of your present on let's say all the confidence
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in the provide technology and services in many many other countries
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we're almost one hundred your company and your basic living in switzerland
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and also most of our our in these actually section base here
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no oh well um as a sense uh earlier um
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we are in the um in the business i mean even interest but enabling trust
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these is difficult you know trust difficult to achieve it's difficult but even physical walls
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it's even more challenging to achieve that in the field in the in the digital world and and
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some of the challenges that our our customers uh and partners have are in the main on digital sound
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so when we say like digital some anti what do we mean by that it's about controlling
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the tacit and personal data it's about ability to act independently in the in the digital world
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uh also ability to exercise control uh in in in the ability to exercise
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you know some rules and regulations in the the the new the mean of control
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and when we talk about the society what mostly what implement in the what's uh what sense they is
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basically some of the data cement the rules and regulations such as
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data residency where you have the the regular uh regulations like um
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they're the data is a store uh how's it takes asked how's process that's on also data protection and so on
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and roughly around seventy percent of all countries have implemented
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some sort of uh some level of data some antsy rules
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but besides besides all that we still see a lot of a lot
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of headlines that are like a lot of personal data gets exposed interval
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hack that some of the public agencies and public administration and being it's
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being under you know some kind of hacking or substituted back and so on
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so um you know this by the regulations garments in companies they
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lose control of of the data and data regulation alone is not enough
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and of course when you look at the the emergence of generative yeah and lodging line
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three more lot models they only amplify the problem so now we have even even bigger problems
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and then she said headlines like like this that that change a pity for example you know has an issue sweet uh
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using some private information for for running the models uh also
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tried it but be privacy model and also cooperate and and propositions
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so those problems and now even even the even bigger with the with the emergence of generated yeah
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but still it's you know a eyes uh is very
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interesting and very promising field and and uh let's say
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our customers you know they they will use these technologies that and now just gonna while up quickly to some
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common yeah a fact and use cases um when does that much
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so when you look at the the whole kind of machine learning process
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the usually it's consist of you know some faces and and first you
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have a phase where you really need to uh get the data to
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provide data which is usually done by by some legitimate they don't or
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in our case is it a these are the common so they provided data that data
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gets usually gets normalised uh a clean then
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in some cases anonymous then that goes into the
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training was training is completed this uh then in all the model gives it boils and operate
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it in some uh some production and then gets carried by by um let's say external parties
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um and usually as he like garment you know plays the role of bacon all over so they
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provided data uh some cases they also supervised or
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control the the overall architecture the training architecture of
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and the training and also in some cases they actually operate to the modern production
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but in much much what's the
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um mm what's more often is that's
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the actually the this job of you know training and you know the the the
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data processing training and then uh uh operating it gets don't you buy so is provided
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so what's up subcontractor so now you have you know government service providers who all have access to
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to the data and also to to the training more um and also to the models that as well
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and then of course there's the all races binge robots that age and that was tied to access to the to accept
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to the uh to the architecture of the of the of the model of the training and also also to the mall
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and when you look at the possible threats you know of course there's the
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there can be a fresh from the inside from the you know some kind of
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garment insider that he's acting maliciously either intentionally or non eventually so for example are there
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if they are you know but the word you know computer gets hacked and then the uh hackers get actually access to
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some of the company or government resources for that um
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so there there's there's there's also like a fat from the
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service provider because the service provider usually a private company so they
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have their own um kind of a uh_huh uh_huh they they have
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different teams and so on and for example also provided they can
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um maybe they want to save money save effort so maybe they don't wanna train uh the the
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model so they they will take some similar model from from another customer who for example the cannot achieve
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the accuracy uh all all of the model something
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perhaps they they had some on offer is data
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to to to this uh garment to bathe in order to achieve the accuracy
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that's also those are all kind of an all possible facts and of
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course you know there's a malicious third party and be in you know it's
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and then when it comes to goals it so i'll just give the data to steal the model
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a two one p. m. if performances on to earn money would say money
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these are kind of the the common goals and uh yeah when it comes to techniques it's the
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um is the uh the data manipulation data poisoning
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uh also families so bearing the the model twenty four
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better certain records where um there used to train the model and also reconstruction parents acquiring the
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the model to reconstruct the training data or or the big well
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and the facts in effect can be like financially go
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regular to read a petition also instructions uh uh_huh so um
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when it comes to you know beta some within a i like what what what are the needs of the garments
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oh or government factor oh so first no garment
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one sovereignty over its data and and machine learning wells
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in order to enable inner integrity of the data uh used to use the machine well
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also the protection on on on the rise data that they use but i don't leakage
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or or it's it's um so as i said like we are you know we are
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as a company we also famous is for for physical market solutions but uh in the
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digital domain adventure world a new what the marking technologies are actually require this ditch the will
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uh and this is to watermark you know the the what is the most valuable discusses the which is the bit
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um alright so some some of the solutions that we're now looking into how to actually address the
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stress how we actually we can uh you know that uh uh yeah have we can solve these problems
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so one of the main research questions that the
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um the we are now investigate and and and
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that this can be watermark the data so we can first of all the fact that data leakage
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um this and also can we detect that that there are some
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other models that were trained on the on the bottom or later
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um and also can we detect the leakage source reports that
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and for this we are looking into different things and one of the cases
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a membership interference attack uh so these these things you know aim
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to detect whether known record was used to train the human model
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so there are these techniques are usually based on you know measuring different behaviour of
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the model for non members and non members so mostly due to overheating so it's
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basically based on the fact that it's the uh you know models act differently or act better uh uh
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you are on the train data versus uh on the data that they haven't seen before
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and um we we believe that these can be
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mostly used uh these types of techniques can be most most uses of prevention to to
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find out if your model actually it's the data part to try you pour the model
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or to use it as a detection tool to the discovered another model usually
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this is just like i'm not short short little small but i don't like um
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uh about this so basically the for for mention for for this type of ah
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technique you know of when you train the data you take some samples from
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from the data population than you use it to to to train the the model
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and if you don't train the model in the proper way in this model actually acts um
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act differently or more provides a more accurate information on the train data versus on the test data and then
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this later can be explored by the light yeah sorry
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um in in yeah and then they're going the kind of black box model
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and the other one is the the data set what the marking of
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an these techniques aim to verify the authenticity and integrity of data sets and
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couldn't rights owners usually consists of two phases the the embedding phase where
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that what remote is inserted into the entire data set or sets it with
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and then there's the verification phase better function is a is a different to the ownership of it
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and also we we believe that this type of a a a technique can be used as
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a detection tool to discover than other public mobile actually use use your data for for trade
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uh also on this type of did it you know
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can be a used uh or can be adapted the
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tomb you know some of the records in a way that the market would be assimilated in in the mall
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and yeah just a short diagram a about dates automatic so when you have
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some kind of a data uh data sets are enough for some kind of justification
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then you can add some disturbance or some kind of the key to
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label to its ah so then this becomes kind of like the sample
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then all these data goes into training and then if you really want to actually detect whether the the
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data is being a lead somewhere um you know if
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you actually use this particular sample on this uh um
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on the suspicious model you can actually get the the the information uh about this you
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know you can actually get the the label um uh that that was marked and before
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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