Privacy Mode
@PrivacyMode
Privacy Mode
@PrivacyMode
- Joined Sep 2020
- twitter.com/HasPrivacy
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:
- Omidyar Network India (ONI) - https://hasgeek.com/oni
- Amazon Web Services (AWS) - https://hasgeek.com/aws
- Zeta - https://hasgeek.com/zetasuite
- Google India - https://hasgeek.com/googleindia
- GitHub Inc - https://github.com/
- 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
Upcoming
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Privacy Mode
Privacy practices in the Indian technology ecosystem: A 2020 survey of the makers of products and services
Online -
India's Personal Data Protection (PDP) Bill: Understanding Concerns of Stakeholders
Online -
Privacy Mode fellowship programme: Documenting privacy best practices in industry
Online
All projects
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Policy Reviews: Examining policies around privacy, data governance and usage for being explainable and specific with outcomes
Online -
Testimonials for Privacy Mode: Importance of a community for data privacy for India's technology ecosystem
Online -
Amendments to the IT Rules 2022: Impact on SMEs and SSMIs
Online -
Data Governance Approaches for Business in the EU: Navigating the Tech and Policy Aspects of Regulations
Online -
Ensuring Safe and Effective Healthcare Technologies: Implementation and Enforcement of Medical Device Regulation
Online
Past videos
Supported projects
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The Fifth Elephant
Designing for data ownership, control and regulatory compliance - for enterprises and individuals: Meetup
Bangalore -
Doing data access and management inside organizations: Twitter Spaces
Online -
Rootconf
Telecom regulation in India: Community meetups
New Delhi -
Digital ID and Identifiers working group
Digital Health Records and Patient Data: Exploring the digitization of data, end-uses and responsible design
Online -
Navigating the CERT-In directions for business operations. Regulations, community engagement and change.
Bangalore and Online -
Privacy as Risk Assessment and Risk Mitigation: Learn how to design organizations that manage risk
Online -
One Vote
Examine the role of technology in elections: Call for evidence about risks and benefits of introducing blockchain in India's electoral systems
Online -
One Vote Annual Conference: A conference on technological interventions in elections
Online -
Data Privacy Conference: On building privacy in engineering and product processes.
Online -
Detecting anomalous network patterns: Using anomaly patterns for improved data security, network monitoring and observability.
Online -
Scalable Data Privacy Engineering: Balancing scale, with privacy and utility is doable.
Online -
MLOps Conference: On DataOps, productionizing ML models, and running experiments at scale.
Online
Supported submissions
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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
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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
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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
Past sessions
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