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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Confidential machine learning on untrusted platforms: a survey.

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Confidential machine learning (CML) protects sensitive data during training on untrusted platforms. This survey covers cryptographic methods, challenges, and trade-offs for secure AI development.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cryptography

Background:

  • Data owners increasingly use untrusted platforms for machine learning (ML) due to growing data volumes.
  • Sensitive data and ML models face risks of unauthorized access, misuse, and privacy breaches on these platforms.

Purpose of the Study:

  • To survey and summarize research in confidential machine learning (CML).
  • To provide a unified framework for understanding challenges and innovations in outsourcing ML confidentially.

Main Methods:

  • Focus on cryptographic approaches for confidential ML model training.
  • Include perturbation-based methods and hardware-assisted computing environments for CML.
  • Analyze threat models, security assumptions, design principles, and trade-offs.

Main Results:

  • Identified critical challenges and innovations in confidential ML.
  • Highlighted the trade-offs between data utility, cost, and confidentiality.
  • Covered a range of CML approaches and environments.

Conclusions:

  • CML is crucial for secure ML on untrusted platforms.
  • Understanding the various approaches and their associated trade-offs is essential for practical CML implementation.