Announcing CN-Protect Free Downloadable Software for Privacy-Protection

by | Mar 11, 2019 | CN Announcements, CN-Protect, Data privacy, Differential Privacy, GDPR, HIPAA

We are pleased to announce the launch of CN-Protect as free, downloadable software to create privacy-protected datasets. We believe:

 

  • Protecting consumer privacy is paramount.
  • Satisfying privacy regulations such as HIPAA, GDPR, and CCPA should not sacrifice analytical value.
  • Data scientists, privacy officers, and legal teams should have the ability to easily ensure privacy.

Today’s businesses are faced with data breaches or misuse of consumer information on a regular basis. In response, governments have moved to protect their citizens through regulations like GDPR in Europe and CCPA in California. Organizations are scrambling to comply with these regulations without adversely impacting their business. However, there is no doubt that people’s privacy should not be compromised.

Current approaches to de-identify data such as masking, tokenization, and aggregation can leave data unprotected or without analytical value.

  • Data masking has no analytical use once applied to all values and, if not applied to all values, does not protect against re-identification. Data masking works by replacing existing sensitive information with information that looks real, but is of no use to anyone who might misuse it and is not reversible.
  • Tokenization removes all data utility of the tokenized fields, but re-identification is still possible through untokenized fields. Tokenization replaces sensitive information with a non-sensitive equivalent or a token which can be used to map back to the original data, but without access to the tokenization system, it is impossible to reverse.
  • Aggregation severely reduces the analytical value and if not done correctly can lead to re-identification. Data aggregation summarizes the data in a cumulative fashion such that any one individual is not re-identifiable. However, if the data does not contain enough samples re-identification is still possible.

CN-protect leverages AI and the most advanced anonymization techniques such as optimal k-Anonymity and Differential Privacy to protect your data and maintain analytical value. Furthermore, CN-Protect is easy to adopt, it is available as a downloadable application or plug-in for your favorite data science platform.

With CN-Protect you can:

  • Comply with privacy regulations such as HIPAA, GDPR, and CCPA;
  • Create privacy protected datasets while maintaining analytical value.

There are a variety of privacy models and data quality metrics available that you can choose from depending on your desired application. These privacy models use anonymization techniques to protect private information, while data quality metrics are used to balance those techniques against the analytical value of the data.

The following privacy models are available in CN-Protect:

  • Optimal k-Anonymity;
  • t-Closeness;
  • Differential Privacy, and more.

You will be able to:

  • Specify parameters for the various privacy models that can be applied across your organization and fine-tune for your many applications;
  • Define acceptable levels of privacy risk for your organization and the intended use of your data;
    Get quantifiable metrics that you can use for compliance;
  • Understand the impact of privacy protection on your statistical and machine learning models.

Stay ahead of regulations and protect your data. Download CN-Protect now for a free trial!

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