CryptoNumerics, a Toronto-based enterprise software company, announced the launch of CN-Protect for Data Science which enables data scientists to implement state-of-the-art privacy protection, such as differential privacy, directly into their data science stack while maintaining analytical value.
According to a 2017 Keggle study, two of the top 10 challenges that data scientists face at work are data inaccessibility and privacy regulations, such as GDPR, HIPAA, and CCPA. Additionally, common privacy protection techniques, such as Data Masking, often decimate the analytical value of the data. CN-Protect for Data Science solves these issues by allowing data scientists to seamlessly privacy-protect datasets that retain their analytical value and can subsequently be used for statistical analysis and machine learning.
“Private information that is contained in data is preventing data scientists from obtaining insights that can help meet business goals. They either cannot access the data at all or receive a low quality version which has had the private information removed.” Monica Holboke, Co-founder & CEO CryptoNumerics. “With CN-Protect for Data Science, data scientists can incorporate privacy protection in their workflow with ease and deliver more powerful models to their organization.”
CN-Protect for Data Science is a privacy-protection python library that works with Anaconda, Scikit and Jupyter Notebooks, smoothly integrating into the data scientist workflow. Data scientists will be able to:
- Create and apply customized privacy protection schemes, streamlining the compliance process.
- Preserve analytical value for model building while ensuring privacy protection.
- Implement differential privacy and other state-of-the-art privacy protection techniques using only a few lines of code.
CN-Protect for Data Science follows the successful launch of CN-Protect Desktop App in March. It is part of CryptoNumerics’ efforts to bring insight-preserving data privacy protection to data science platforms and data engineering pipelines while complying with GDPR, HIPAA, and CCPA. CN-Protect editions for SAS, R Studio, Amazon AWS, Microsoft Azure, and Google GCP are coming soon.
CN-Protect for Data Science is available for download at:
CryptoNumerics, based in Toronto, Ontario, enables organizations to use data to gain insights while overcoming privacy and data residency issues. CN-Protect enables enterprises to create a privacy-protected dataset where privacy risk has been balanced with data quality using AI and differential privacy. CN-Insight allows companies to build statistical and machine learning models without re-locating data using Secure Multiparty Computation and Private Set Intersection. The team includes senior executives and experts from Yahoo, IBM, Qualcomm Atheros, Barclays, Fidelity, KPMG, and more.
SOURCE Cryptonumerics Inc.