Strange Loop Conference | "Demystifying Privacy Preserving Computing" by Tejas Chopra (Strange Loop 2022) @StrangeLoopConf | Uploaded 1 year ago | Updated 1 day ago
When it comes to privacy, encrypted data-in-transit (eg: HTTPS) or encrypted data-at-rest (eg: encrypted hard-disks) schemes provide sufficient cryptographic guarantees in the battle to protect it. The unresolved problem is encrypting data-in-use. Currently, in order to process data, we need to decrypt, process, and re-encrypt. Computation over unencrypted data may compromise the confidentiality of data and suffer various security attacks Privacy-Preserving Computing (PPC) has emerged in recent years to enable the secure computation of the data without revealing the content of the data. These techniques look at how to represent data in a form that can be shared, analyzed, and operated on without exposing the raw information We will discuss current state-of-the-art PPC techniques and the distinct threat models and business use cases they address. The techniques we will cover are: Secure multiparty computation (SMPC), Fully homomorphic encryption (FHE), Differential privacy (DP)
Tejas Chopra
Senior Software Engineer, Netflix
Tejas Chopra is a Senior Software Engineer, working in the Data Storage Platform team at Netflix, where he is responsible for architecting storage solutions to support Netflix Studios and Netflix Streaming Platform. Prior to Netflix, Tejas was working on designing and implementing the storage infrastructure at Box, Inc. to support a cloud content management platform that scales to petabytes of storage & millions of users. Tejas has worked on distributed file systems & backend architectures, both in on-premise and cloud environments as part of several startups in his career. Tejas is an International Keynote Speaker and periodically conducts seminars on Micro services, NFTs, Software Development & Cloud Computing and has a Masters Degree in Electrical & Computer Engineering from Carnegie Mellon University, with a specialization in Computer Systems.
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When it comes to privacy, encrypted data-in-transit (eg: HTTPS) or encrypted data-at-rest (eg: encrypted hard-disks) schemes provide sufficient cryptographic guarantees in the battle to protect it. The unresolved problem is encrypting data-in-use. Currently, in order to process data, we need to decrypt, process, and re-encrypt. Computation over unencrypted data may compromise the confidentiality of data and suffer various security attacks Privacy-Preserving Computing (PPC) has emerged in recent years to enable the secure computation of the data without revealing the content of the data. These techniques look at how to represent data in a form that can be shared, analyzed, and operated on without exposing the raw information We will discuss current state-of-the-art PPC techniques and the distinct threat models and business use cases they address. The techniques we will cover are: Secure multiparty computation (SMPC), Fully homomorphic encryption (FHE), Differential privacy (DP)
Tejas Chopra
Senior Software Engineer, Netflix
Tejas Chopra is a Senior Software Engineer, working in the Data Storage Platform team at Netflix, where he is responsible for architecting storage solutions to support Netflix Studios and Netflix Streaming Platform. Prior to Netflix, Tejas was working on designing and implementing the storage infrastructure at Box, Inc. to support a cloud content management platform that scales to petabytes of storage & millions of users. Tejas has worked on distributed file systems & backend architectures, both in on-premise and cloud environments as part of several startups in his career. Tejas is an International Keynote Speaker and periodically conducts seminars on Micro services, NFTs, Software Development & Cloud Computing and has a Masters Degree in Electrical & Computer Engineering from Carnegie Mellon University, with a specialization in Computer Systems.
------ Sponsored by: ------
Stream is the # 1 Chat API for custom messaging apps. Activate your free 30-day trial to explore Stream Chat. https://gstrm.io/tsl
