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SecureRedact Brings Video Anonymisation to All

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SecureRedact Platform from Pimloc Allows Anyone to Anonymise Video Using Machine-Learning

Policies like GDPR and California’s Consumer Privacy Act have made the conversation around personal data more active and healthy than ever before. And yet, while a text string like your email address or birthday is easily fixed, the same organisation might hold thousands of hours of video, full of innocent faces.

Pimloc is launching SecureRedact, a platform that makes video anonymous by default. From law enforcement agencies to security, smart cities and global brands, teams can now use machine-learning to make their video anonymous in an instant.

Our appearance is our most recognisable biometric personal data. But it’s also one of the hardest to anonymise manually, requiring big teams to trawl through footage, and with significant risk of missing something.

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What’s the difference between a video showing you were in a certain place at a certain time and the location data on your phone? This isn’t just about some abstract piece of digital data, this is one of the most important, biometric and unchangeable aspects of identity.

The conversation has become digitally-focused, and video is the link that locates you in the real world.

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Key details:

●   Available in the cloud, with the option to host on premise where needed

●   Sign up to try the engine for free

●   Usage based pricing

The challenges of anonymous video

Facial recognition is nothing new – but in most cases, the algorithms are trained to identify and profile individuals, not to remove them. SecureRedact is designed for the exact opposite purpose, and errs on the side of caution to ensure no frames of facial privacy are missed.

Furthermore, it has been developed to thrive against even the most messy and difficult to process video, from sources like CCTV and vehicle-mounted cameras in all weather and lighting conditions. And it can be refined by learning from each organisation’s specific data to improve this further.

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