Secure Data Sharing: Homomorphic Encryption and Confidential Computing
E145 | Sun 16 Jul 11:30 a.m.–noon
Presented by
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Surya is a Data Scientist, currently working on the Emerging Technologies team at Red Hat. He is experienced in the field of Machine Learning and Artificial Intelligence. He spent the past year developing models for gaining customer insights, navigating open source tools for data scientists, and doing NLP using transformers models.
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AAKANKSHA DUGGAL
@DuggalAakanksha
Aakanksha Duggal is a Senior Data Scientist in the Emerging Technologies Group at Red Hat. She is a part of the Data Science team and works on developing open source software that uses AI and machine learning applications to solve engineering problems.
AAKANKSHA DUGGAL
@DuggalAakanksha
Abstract
There are over 5 trillion megabytes of data on the internet, and private information and data from phones and laptops are all over the internet. We often tend to accept the privacy policies of various websites without even looking and hence causing a transfer of information to the world.
However, some websites and platforms allow you to anonymize your personal information and still allow these websites to make inferences and analyze the data via Data anonymization. Using this capability of securing and ensuring almost encrypting personally identifiable data in a dataset, we can make the data live in the open source world.
Such is the concept of Homomorphic Encryption, it allows us to eliminate the tradeoff between data usability and privacy, and keep it safe, secure, and private even in the most untrusted environments, like public clouds or external parties. In this session, we will cover what is Homomorphic Encryption and how this can change the outlook on Open Source Data. We will also demonstrate the intersection of AI and how holomorphic encryption can enable multi-party data sharing.
There are over 5 trillion megabytes of data on the internet, and private information and data from phones and laptops are all over the internet. We often tend to accept the privacy policies of various websites without even looking and hence causing a transfer of information to the world. However, some websites and platforms allow you to anonymize your personal information and still allow these websites to make inferences and analyze the data via Data anonymization. Using this capability of securing and ensuring almost encrypting personally identifiable data in a dataset, we can make the data live in the open source world. Such is the concept of Homomorphic Encryption, it allows us to eliminate the tradeoff between data usability and privacy, and keep it safe, secure, and private even in the most untrusted environments, like public clouds or external parties. In this session, we will cover what is Homomorphic Encryption and how this can change the outlook on Open Source Data. We will also demonstrate the intersection of AI and how holomorphic encryption can enable multi-party data sharing.