Traceable or stored biometric information is a computerized invasive method that able to simulate human attendance by mimicking the adaptability of the living persons using their enduring physical or  behavioral characteristics, as a result of the fact that biometrics offer irrefutable evidence of one’s identity. Biometric properties from the perspective of traces or permanent storage can now lead to undesired identification via attendance simulation or tracing of the activities of an individual, because of the power of computers. The “pseudo state of a person being presence” made by the biometric simulation system is able to mimic the living persons attendance even if the legitimate owner of the enrolled biometrics information, is not aware of this process or not physically present in front of the biometric system…

Even when we see an unusual activity we have no practical way to point an object that has been assimilated into the crowd.

Innovya provides a solution to this problem 

Patent (By Micha Shafir): ‘Traceless Biometrics' - Finding any object on the street anonymously without harming privacy by compering recorded, objects from crime scene - using fuzzy and machine learning algorithms.  

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Sustainable University & Traceless Biometrics 

Taking The Streets to the Cyber - Without Harming Privacy


INNOVYA creates networks that provide Public Safety and Smart City applications to protect the Public Sphere – by installing Smart Street lighting with real-time Virtual Patrol and machine learning fuzzy algorithms. Taking realtime street view into the future - Forget the internet of things – we need an internet of Objects​, and Streets (IoS). 

INNOVYA - Traceless Biometric System is using a fuzzy algorithm method/logic for identifying an individual through a biometric identifier that is designed to be non-unique, in the end of road it will be implemented in projects of Cybersecurity and Homeland Security (HLS).

The world is changing right in front of our eyes, going through a turbulence regarding the security of innocent citizens in the public sphere. It is not just Homeland Security anymore. In our new R&D projects we started to apply all over the public sphere virtual patrol. We can now prevent and track crime activities, by using patented virtual cyber street patrol to support the national Action plan, using our machine learning algorithms. We can track suspects back and forward in time, from the crime scene to his/her home base using our global stitching video system, very similar to "Google Street View" but live, using video virtual patrol. (Approved patent)

We live in a world with crime and terrorism, where public safety is a main concern for Governments and Private entities.

Cameras currently in use do not “push” AI information, to Control and Command centers, nor 360° VR view and are often of poor quality. These cameras do not make autonomic decisions; require individual review and they flood data-centers with useless and overload of information.
Current public lighting today is expensive to operate, presents poor quality and pollution. 

Fuzzy Machine Learning logic is a type of mathematics algorithms and programming that more accurately represents how the human brain categorizes objects, evaluates conditions, and processes decisions. Fuzzy logic allows an object to belong to a set to a certain degree or with a certain confidence. Instead of using unique information, an amorphous identifier(s) agent is replacing it. The amorphous agent is an incomplete identifier obtained from a fresh scanned information which is non-unique to prevent privacy violation. Another alterable limit indicator(s) can be added to overturn non-unique combinations to become unique. By ‘incomplete’ or ‘alterable’ we mean that the information itself cannot be reconstructed from the identifier(s) even with the device that originally allocated the agent or the Identifier. Using this method, the object has to be present (during the identification process since the (secret) token identifier itself has no true value except in a particular identification transaction. This is important in order to avoid an association with recorded values or any other unique characteristic.