Privacy-Enhancing Technologies: Who Should Care and Why?

Description: 

The biggest problems facing healthcare and finserv professionals in 2022? Data problems…

We hear from C-Suite and compliance officers, data scientists and even cloud architects that if your work includes machine learning or analytics, you’re likely facing data access, data prep, and data bias challenges –– along with a host of compliance requirements and more. What if emerging privacy-enhancing technologies could reshape and catalyze your organizations’ data-based innovations?

Speakers include: 

• Chris Barnett, VP, Partnerships & Marketing, TripleBlind 
• Chad Lagomarsino, Partnership Engineer, TripleBlind
• Mitchell Roberts, Director, Product Marketing, TripleBlind

Date/Time: Wednesday, May 25th, 11:00 am CT / 12:00 pm EST

According to Gartner¹ – the three key use cases for Privacy Enhancing Technologies are:

1. AI model training and sharing models with third parties.
2. Usage of public cloud platforms amid data residency restrictions.
3. Internal and external and business intelligence activities. 

According to MITRE², who operates federally funded data R&D centers:

“The most valuable insights come from applying highly valuable analytics to shared data across multiple organizations, which increases the risk of exposing private information or algorithms. This three-way bind – balancing the individual needs of privacy, the analyst’s needs of generating insight and the inventor’s needs of protecting analytics – has been hard to balance…”

The emergent category of privacy-enhancing technologies (PET), also referred to as privacy preserving technologies or privacy-enhancing computation (PEC), represents a cohort of technological solutions which seek to ease the pains, pressures, and risks involved in working with sensitive and protected data. 

In this webinar, hear TripleBlind’s experts discuss how to choose the optimal PET technique(s) for your business problem and use cases – and how to evaluate and implement solutions that will have the greatest impact. Techniques covered include:

1. Differential Privacy
2. Federated Learning
3. Homomorphic Encryption
4. Secure Enclaves (aka Confidential Compute or Trusted Execution Environment)
5. Secure Multi-party Computation
6. Synthetic Data
7. Tokenization (along with data masking and data hashing)

REFERENCES:

1. Three Critical Use Cases for Privacy-Enhancing Computation Techniques, Bart Williamsen, et al, 28 June 2021.
2. Federated Approaches to Observational Research – Technical Considerations, Stacy Chen, Zeshan Rajput, Nichole Persing, February 2022.