Privacy Analytics



Privacy Analytics

November 15, 2012 09:00 ET

Privacy Analytics Expanding HIPAA Certified Data to Organizations Worldwide

OTTAWA, ONTARIO--(Marketwire - Nov. 15, 2012) -

The provider of the world's only commercially available data-masking software and de-identification software is at it again, this time refining their algorithms to allow for even more fine-tuned control over your data. With the latest refinements to their data-masking software, it's never been easier for companies worldwide to obfuscate their data for inclusion in their statistical analysis and reports.

Back in 2007, Privacy Analytics was formed as an offshoot from the Electronic Health information Laboratory (EHIL), one of the leaders among the scientific community working in the area of re-identification risk assessment and de-identification. During the past 5 years, Privacy Analytics has grown to be the world leader in making strong data obfuscation software and de-identification a simple process.

Data-Masking and De-Identification Software Created By Professionals In The Field

When working with mission critical data that has to be held to the strictest privacy standards one must take into account a number of factors. Those who are not in the industry would undoubtedly miss some of this and mistakes would happen. Privacy Analytics mitigates this threat by employing computer scientists and analysts that continually assess and reassess their software and practices to ensure that the data that is returned using their software is cleaner than what any other similar company can provide.

Working to strict Canadian and International Standards like HIPAA Privacy Analytics software is the market leader in de-identification.

To be honest, it's the only software that combines both de-identification and data-masking into one completely rounded tool that's easy to use and understand whether your company is dealing with medical data, statistical data or otherwise sensitive information. No matter what data you need to remove identifying markers from, Privacy Analytics has the tools required.

Working With Big Data Sets

Source and origin are a common concern when dealing with sensitive data. Data collection can be processed by a number of complex sources. There are databanks and data brokers which provide public and private data including government agencies. Much can be said for even the smallest amount of information. For example, one's income level and education could be assumed simply based on their ZIP or postal code.

There are legal frameworks set to allow the use of collected data that has been de-identified or anonymized which can be used without consent of the individual. De-identification allows you to control and obfuscate data in this case for not only the source data but also including linked databases. There is always a need to evaluate de-identification to ensure identifiability is low to keep a system compliant. Privacy Analytics is the best in the field when it comes to working with big data sets and keeping information safe and secure.

ABOUT PRIVACYANALYTICS.COM

Founded by Dr. Khaled El Emam, a pioneer in the field of healthcare related privacy and data privacy standards and the Canada Research Chair in Electronic Health Information at the University of Ottawa, as well as being an Associate Professor in the Faculty of Medicine at the University. Dr. El Emam is currently the senior scientist at the Children's Hospital of Eastern Ontario Research Institute and is the team leader for the multi-disciplinary Electronic Health Information Laboratory (EHIL). Combined with these accreditations, Dr. El Emam was also ranked the top systems and software engineering scholar worldwide by The Journal of Systems and Software in 2003 and 2004. Combining the knowledge he's gained in the field and through his research, Dr. El Emam has pioneered the data-masking and de-identification market with his company Privacy Analytics. Visit the Privacy Analytics website today to find out what the software can do for your statistical analysis and data masking needs.

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