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Privacy Enhancing Computation

Privacy-enhancing computation (also known as secure multi-party computation) is a field of computer science that focuses on designing and implementing methods for multiple parties to jointly compute a function over their private inputs without revealing anything about the inputs to each other except for the output of the function.

Applications

Applications of privacy-enhancing computation include secure online voting, secure auctions, and secure machine learning. One example of an application of privacy-enhancing computation is homomorphic encryption, which allows computation to be performed directly on encrypted data without the need to decrypt it first. Another example is secure multiparty computation protocols such as SPDZ and Fairplay, which allow multiple parties to compute functions jointly over their private inputs without revealing them to each other.

security
safe

Privacy-enhancing computation (PEC) refers to a set of technologies and techniques that enable individuals and organizations to perform computations on sensitive data without revealing the data itself to the computational party. PEC has a wide range of implementations, including:

  • It provide tools for implementing differential privacy in various programming languages. Examples include the R package "dpyr" and the Python library "TensorFlow/privacy."

    differential privacy libraries

  • Allows for computations to be performed on encrypted data without the need to decrypt it first.

    homomorphic encryption

  • A mathematical framework for adding noise to data in a way that preserves its privacy while still allowing statistical analyses to be performed on it.

    differential privacy

  • These allow multiple parties to perform computations on private data without revealing it to each other. Examples include Sharemind, SPDZ, and Quicklinks.

    smc frameworks

  • These provide tools for implementing homomorphic encryption in various programming languages. Examples include HElib, SEAL, and Palisade.

    HOMOMORPHIC ENCRYPTION LIBRARIES

  • It provide tools for implementing differential privacy in various programming languages. Examples include the R package "dpyr" and the Python library "TensorFlow/privacy."

    differential privacy libraries

case studies

  • A consortium of banks used homomorphic encryption to enable secure collaboration on financial risk analysis without revealing sensitive financial data to each other.

    (Bazaka et al., 2019)

  • Researchers at Harvard University used secure multiparty computation to accurately estimate income distribution in the US without revealin the income data of any individual taxplayer.

    (Chaum et al.,2017)

  • Researchers at the Max Planck Institute for Software Systems used differential privacy to enable the release of large-scale population statistics while preserving the privacy of individual respondents.

    (Erlingsson et al., 2018)

  • A consortium of banks used homomorphic encryption to enable secure collaboration on financial risk analysis without revealing sensitive financial data to each other.

    (Bazaka et al., 2019)

Protect your data and privacy with privacy-enhancing computation.

Keep your sensitive information confidential and secure.

Use secure multi-party computation to perform calculations on data without revealing the underlying data to any of the parties involved.

Ensure that your data is only used for the intended purpose, without being shared or sold to third parties.

Use homomorphic encryption to perform calculations on encrypted data without ever decrypting the data.

Benefit from the increased security and privacy provided by privacy-enhancing computation in various applications, including finance, healthcare, and government services.

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