Announcing CN-Protect Free Downloadable Software for Privacy-Protection

Announcing CN-Protect Free Downloadable Software for Privacy-Protection

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!

Weekly News #1

Weekly News #1

Marriot and Quora Data breach

US legislators are proposing fines and jail for CEO’s of breached companies after the data breach of Marriot and QuoraThe US Census Bureau is using differential privacy to protect data privacy while allowing data analysis. FTI Consulting is offering a Data-Protection-Officer-as-a-service.

In just one week, we learned about two major data breaches, at Marriott and Quora, compromising the data of 600 million people. In 2018, there have been 15 data breaches, 8 more than in 2017.

Marriott has lost $4.08 billion in market value since November 29th, when the breach was reported; however, this loss could potentially worsen because of fines and lawsuits. Under GDPR, Marriott could be fined with $912 million, and there is a $12.5 billion damages lawsuit in the process.

Quora reported on Monday that hackers had gained access to the data of 100 million users. The information comprised names, email addresses, passwords, and data from social networks.

All these breaches have pushed legislators in the US to propose bills that would fine not only the affected companies but also the CEOs. Senator Ron Wyden’s proposal includes up to 20 years of jail for chief execs and $5 million fines for CEOs.

However, there are processes and technologies that can help organizations protect their customers’ data privacy.

One solution is to designate a Data Protection Officer (DPO), a role that was introduced by the GDPR. While not every company is required to have a DPO, having someone in charge of data privacy and protection is a must. FTI Consulting is now offering DPO-as-a-service to help companies satisfy regulatory requirements.

Another solution is to use technologies, such as differential privacy, to keep the data private. Differential privacy is already used by companies like Apple and Google, but one of the earliest adopters is the Census Bureau. By mandate, the Bureau has to keep each person’s information private and to provide useful data, and Differential Privacy allows it to do so.

No single solution is a silver bullet, but a combination of privacy-preserving technologies and processes will help organizations protect their customers’ data privacy.