Privacy Mode

Privacy Mode

@PrivacyMode

Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences.

Sponsors: Privacy Mode’s programmes are sponsored by:

  1. Omidyar Network India (ONI) - https://hasgeek.com/oni
  2. Amazon Web Services (AWS) - https://hasgeek.com/aws
  3. Zeta - https://hasgeek.com/zetasuite
  4. Google India - https://hasgeek.com/googleindia
  5. GitHub Inc - https://github.com/
  6. Facebook - https://about.fb.com

Sponsors do not have a say in editorial decisions, nor have access to participants’ data.

Contact information: Follow Privacy Mode on Twitter

Supported projects

Show more

Supported submissions

  • MLOps Conference

    Privacy Attacks in Machine Learning Systems - Discover, Detect and Defend

    My name is Upendra Singh. I work at Twilio as an Architect. As a part of this talk proposal I would like to shed some light on the new kind of attacks machine learning systems are facing nowadays - Privacy Attacks. During the talk we will explain and demonstrate how to discover, detect and defend Privacy related vulnerabilities in our machine learning models. Will also explain why it is so critic… more
    • 24 comments
    • Confirmed & scheduled
    • 17 Apr 2021

  • MLOps Conference

    Fighting Fraudsters in Email Communication at Twilio using Machine Learning

    My name is Sachin Nagargoje. I work at Twilio as a Staff Data Scientist. As a part of this talk proposal I would like to shed some light on the kind of attacks we are facing at Twilio nowadays and how we are tackling it via different innovative ways and Machine Learning techniques. I want to showcase what are the challenges we face, and how we do and what we do to catch such unwanted communicatio… more
    • 10 comments
    • Confirmed & scheduled
    • 02 Jun 2021

  • MLOps Conference

    Fairness in ML: How do we build unbiased ML workflows?

    Biases often arise in automated workflows based on MachineLearning models due to erroneous assumptions made in the learning process. Examples of such biases involve societal biases such as gender bias, racial bias, age bias and so on. more
    • 3 comments
    • Confirmed & scheduled
    • 29 Jun 2021

Upcoming

Accepting submissions

Show more

All projects

{{ title }}

{{ heading }}

{{ project.datetime }}

{{ project.title }}

{{ project.venue }}