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Related Concept Videos

Dot Product01:29

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The dot product is an essential concept in mathematics and physics.
In engineering, the dot product of any two vectors is the product of the magnitudes of the vectors and the cosine of the angle between them. It is denoted by a dot symbol between the two vectors.
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Dot Product: Problem Solving01:21

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The dot product is a powerful tool in problem-solving involving vectors, given that the dot product of two vectors is the product of their magnitudes and the cosine of the angle between them measured anti-clockwise. Solving problems involving the dot product requires understanding its properties and developing a step-by-step process to solve them. Here are the main steps to follow when solving any general problem involving the dot product:
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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Efficient Lp Distance Computation Using Function-Hiding Inner Product Encryption for Privacy-Preserving Anomaly

Dong-Hyeon Ryu1, Seong-Yun Jeon2, Junho Hong3

  • 1Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.

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This study introduces a privacy-preserving method for anomaly detection in Internet of Things (IoT) systems. It enables secure computation of Lp distance, making IoT device verification feasible without compromising sensitive data.

Keywords:
anomaly detectionfunctional encryptionmean p-powered error

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Area of Science:

  • Computer Science
  • Cryptography
  • Internet of Things

Background:

  • Anomaly detection is crucial for verifying Internet of Things (IoT) device operation.
  • Resource constraints on individual IoT devices limit local anomaly detection capabilities.
  • Outsourcing anomaly detection to servers raises privacy concerns due to data sharing.

Purpose of the Study:

  • To propose a privacy-preserving method for computing Lp distance, even for p>2.
  • To apply this method for calculating p-powered error for anomaly detection in IoT systems.
  • To ensure the feasibility and efficiency of the proposed method on resource-constrained devices.

Main Methods:

  • Utilizing inner product functional encryption to compute Lp distance privately.
  • Developing a privacy-preserving metric (p-powered error) for anomaly detection.
  • Implementing and testing the method on desktop and Raspberry Pi devices.

Main Results:

  • The proposed method enables private computation of Lp distance for anomaly detection.
  • Demonstrated feasibility and efficiency on both desktop and embedded systems (Raspberry Pi).
  • The method is suitable for real-world IoT applications with privacy requirements.

Conclusions:

  • The developed technique offers a viable solution for privacy-preserving anomaly detection in IoT.
  • Potential applications include smart building management and remote device diagnostics.
  • This approach addresses the trade-off between IoT device verification and data privacy.