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NASSCOM ER&D Webinar - Securing Machine Learning Models | 24th November, 2020 | 4:00 pm - 5:00 pm

28th Nov, 2024

06:19PM to 06:19PM IST

Virtual

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NASSCOM: Securing Machine Learning Models | Date: 24th November 2020 | Time: 4:00 pm - 5:00 pm | Time: 3:00 pm - 4:30 pm
NASSCOM Engineering R&D is pleased to invite you for joining a webinar on “Securing Machine Learning Models” on 24th November, 2020 (Tuesday) from 04:00 PM – 05:00 PM (IST).
ML models are popping up everywhere around us, be it e-commerce, networks or healthcare. We went through a journey of running these models on a local system to industrializing these models and scaling them to serve millions of users using cutting edge cloud technologies. However, very few people actually realize how easy or difficult these models are to hack & replicate using various black box & white box methodologies.
This talk will walk you through important security aspects one has to keep in mind while deploying machine learning models on cloud, edge or on-premise. We will also showcase counter measures to defend these attacks as well. We share undertake the standard security expert’s approach of a) Awareness b) Applicability c) Countermeasures.
The talk will majorly focus attacks like:
BulletModel extraction – How can an adversary replicate your model?
BulletModel evasion / adversarial attacks – How can an adversary corrupt your model?
BulletModel watermarking – How can one prove ownership of a model?
The session would also showcase demo of these attacks on a variety of models and datasets, along with defense mechanisms.
AGENDA
TimeSpeakerTopic
5 minsOverview & Context SettingNASSCOM ER&D Team
45 minsSession – Securing Machine Learning Models Raghotham Sripadraj – Senior Data Scientist, Ericsson GAIA

Rajib Biswas – Lead Data Scientist, Ericsson GAIA

10 minsQ&A Session

 

Speakers’ Profile

Raghotham SripadrajRaghotham Sripadraj
Senior Data Scientist,
Ericsson GAIA

 

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Rajib BiswasRajib Biswas
Lead Data Scientist working,

Ericsson GAIA

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Registration
Participation is free for all,
but prior registration is mandatory. Click here to register.
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