Bell Labs Internship on Confidential Data Preparation for Secure ML Workflows (PhD)

Reposted 6 Days Ago
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2 Locations
In-Office or Remote
Internship
Software
The Role
This internship focuses on designing solutions for secure data preparation in ML workflows, ensuring data privacy while maintaining model performance. Interns will prototype methods using advanced technologies like Trusted Execution Environments and contribute to academic publications.
Summary Generated by Built In

Machine Learning (ML) pipelines rely heavily on high-quality data processing, including normalization, anomaly correction, and formatting, to ensure accurate and reliable model performance. However, many datasets used in ML applications are confidential and private, posing significant challenges to traditional data preparation methods. This internship addresses the critical need for innovative solutions that enable secure and efficient data preparation without compromising data privacy.

 

The primary goal of this internship is to design and implement a solution for oblivious data preparation. This approach would allow the modification and cleaning of sensitive datasets without requiring direct access to the raw data. By leveraging technologies such as Trusted Execution Environments (TEE) and trusted hardware, the intern will explore methods to ensure that data remains secure throughout the preprocessing pipeline while still meeting the stringent requirements of ML workflows.

 

This internship offers an exciting opportunity to work at the intersection of data privacy, security, and machine learning. The selected candidate will gain hands-on experience with cutting-edge technologies, contribute to solving real-world privacy challenges, and help pave the way for secure ML applications in industries where data confidentiality is paramount. Ideally, this project will lead to a publication at a top academic venue.

Responsibilities
  • You will get familiar with the problem by studying existing state-of-the-art regarding data preparation in ML workflows and confidential computation techniques.

  • You will develop a prototype solution for confidential data cleaning, including defining the threat model and system assumptions relevant to this problem.

  • You will be involved in writing an academic paper describing the solution.

Location: Stuttgart (Germany) 

Qualifications
  • Student enrolled in a PhD of Computer Science/Engineering

  • Strong programming skills in Python and ML frameworks (PyTorch / TensorFlow)

  • Experience with implementation of ML pipelines is highly desirable

  • Familiarity with TEE, cryptographic protocols and/or secure systems is a plus

  • A strong publication record is a big plus

  • Language skill: English

Top Skills

Cryptographic Protocols
Python
PyTorch
TensorFlow
Trusted Execution Environments
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The Company
Dallas, Texas
132,624 Employees

What We Do

At Nokia, we create technology that helps the world act together. As a trusted partner for critical networks, we are committed to innovation and technology leadership across mobile, fixed and cloud networks. We create value with intellectual property and long-term research, led by the award-winning Nokia Bell Labs. Adhering to the highest standards of integrity and security, we help build the capabilities needed for a more productive, sustainable and inclusive world.

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