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World Congress 2024 • Aug 20, 2024 • Session details

TikTok's Privacy Innovation

Mingshen Sun

How do you train accurate models without exposing raw data? TikTok solved this. Their open-source clean room uses synthetic data and hardware isolation to guarantee zero leakage.

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#1 about 3 min

Data collaboration workflows and multi-party architectures

Enabling multiple data providers and consumers to interact via controlled platform policies.

#2 about 2 min

Security and privacy goals for data collaboration platforms

Balancing data confidentiality and targeted access controls with interactive usability and accuracy.

#3 about 3 min

Evaluating existing privacy enhancing technology limitations

Standard approaches like SQL clean rooms, differential privacy, and raw hardware isolation fail to balance usability with strict privacy guarantees.

#4 about 3 min

Designing a two-stage confidential data clean room

Splitting workloads into a mock-data programming stage and a secure-hardware execution stage preserves both analytic flexibility and data security.

#5 about 2 min

Guaranteeing integrity through hardware trusted execution environments

Integrating hardware-level isolation allows programmatic access to accurate datasets without exposing the raw information to individual researchers.

#6 about 3 min

Cloud infrastructure deployment and industry use cases

Leveraging cloud-provided confidential compute instances supports secure research environments, precise ad measurement, and privacy-preserving machine learning.

#7 about 2 min

Configuring synthetic data for safe interactive programming

Processing public datasets through high-quality differential privacy algorithms generates safe proxies for early-stage workload development.

#8 about 3 min

Executing remote data exploration and model training

Researchers analyze data distributions and schedule predictive machine learning jobs securely onto isolated backend hardware environments.

#9 about 1 min

Validating hardware execution using secure attestation tokens

Downloading and verifying cryptographically signed hardware reports ensures analytic workloads run exclusively inside a verified confidential space.

#10 about 3 min

Advancing confidential computing with open source multi-way collaboration

Supporting multi-party architectures alongside automated synthetic data provisioning and upcoming isolated GPU computing capabilities broadens trusted execution adoption.

Matching moments

2:17 min

Blending trusted hardware with future security strategies

Liz Moy · LIVE

17:05 min

Navigating data privacy boundaries and adversarial model reliability

Alexandra Waldherr · LIVE

4:04 min

Embedding data security and applied ethics into developer education

Daniel Tao +3 · WWC 2024

4:08 min

Securing data in use with confidential cloud computing

Isha Salania Isha Salania · WWC Europe 2026

2:06 min

Demonstrating transparency and privacy protection in machine learning

Toju Duke · WWC 2022

5:34 min

Addressing audience concerns on licensing and privacy

Thomas Dohmke Thomas Dohmke · WWC 2022

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