Today, the privacy of individuals’ data is at ever-increasing risk. Specifically:

  • Traditional business models are threatened.
  • New data uses are significantly restricted.
  • Opportunities to save lives are substantially limited.

There is a specific reason for these risks: data often has insufficient context – or no context at all. What this means is that there is no way to know:

  • How important a particular piece of data is to a person’s privacy.
  • How important that data is to health or other research.
  • Where that data needs to be protected and where it doesn’t.
  • When that data needs to be protected and when it doesn’t.
  • The degree of protection that data needs (which can vary, based on the data subject, the geographic- or entity-specific place of use, and the time of use).

This lack of context granularity makes it difficult or impossible to digitally enforce or audit privacy policies. This has already caused major problems. Examples include:

  • Threats to the use of Big Data analytics necessary to support initiatives that involve use or sharing of restricted and sensitive information.
  • The largest IoT wearables company disclosed that future revenues will be reduced due to inabilities to fully use data because of privacy risks.
  • Restrictions on data use due to privacy risks limit the value of data gathered by fitness, health and medical device manufacturers.
  • The profitability of international data-centric companies is threatened by new EU GDPR data protection requirements.
  • Major financial institutions were recently fined for not digitally enforcing privacy and security policies.
  • Growing liability, fines and exposure for data breaches - healthcare and otherwise – have become the new normal.

Anonos Big Privacy software is disruptive because, by granularizing the context of all data, it also de-risks it, enabling privacy policies to be digitally enforced and audited. This context-specificity and awareness means that companies can analyze, combine and share data without compromising privacy. Most important, it allows them to maximize the full value of data across the entire lifecycle of that data.

By technically enforcing privacy policies using Digital Rights Management (DRM) controls, the full value of data is maximized over the entire life cycle of the data.

Anonos BigPrivacy technology retains full value and utility of restricted/sensitive data to support authorized use cases, all while minimizing the risk of data misuse, abuse or compromise. We call this process "Anonosizing" data.

BigPrivacy Technology De-Risks Repurposing and Sharing of Restricted/Sensitive Data

  • To maximize the value of restricted/sensitive data, one must share and use that data for new purposes.
  • Nonintegrated approaches to data security and privacy leave significant gaps in protection because they serve very different purposes.
  • Managing liability risks from sharing and repurposing restricted/sensitive data requires fusing together data security and privacy.

latest insights

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Trusted Advertising White Paper

November 2014

comment letters

NIST Comment Letter – De­-Identification of Personally Identifiable Information

May 2015

news articles

NIST Comment Letter – De­-Identification of Personally Identifiable Information

November 2014


Anonos at the 8th International Computers, Privacy and Data Protection (CPDP) Conference in Brussels Video Presentation

January 2015

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