Tracking entities by means of hash values

• US 10,042,818 B2
• Filed: 11/18/2016
• Issued: 08/07/2018
• Est. Priority Date: 01/30/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Albano, Gherardo, De Judicibus, Dario
• Primary Examiner(s): BLOSS, STEPHANIE E
• Application Number:
  US15/355,237
• Publication Number:
  US 20170068641A1
• Time in Patent Office: 627 Days
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 17/13 Differential equations using digital differential analysers G06F7/64
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 20/625 License plates
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G06V 40/166 using acquisition arrangements
• CPC Class Codes: G07C 1/10 together with the recording, indicating or registering of other data, e.g. of signs of identity
• CPC Class Codes: G10L 17/00 Speaker identification or verification techniques

Abstract

Mechanisms for tracking an entity are provided. A time is determined by a sensor having a clock, the time being within a time slot in a series of time slots. First data of the time slot is provided and shared between a plurality of sensors. The sensor receives data from the movable entity. The sensor calculates identifying data from the received data for identifying the entity. Derivative identifying data is calculated by applying a modifying function using the provided first data for modifying the identifying data. The sensor calculates a hash value by taking the derivative identifying data as input. The sensor sends a message to a central server for determining the position of the entity, the message comprising the hash value and an identifier of the sensor.

Claims

1. A computer-implemented method, performed in a first sensor device, for tracking a movable entity, wherein the movable entity is a physical entity within a physical space, the method comprising:

generating, by the first sensor device, a set of data based on sensing of one or more attributes of the movable entity present in the physical space;

calculating, by the first sensor device, identifying data from the generated set of data, wherein the identifying data is a reduced size set of data relative to the generated set of data and is directed to identifying characteristics of the movable entity within the generated set of data;

calculating, by the first sensor device, derivative identifying data by applying a modifying function on the identifying data, wherein the derivative identifying data is anonymized data, calculated from the identifying data, that does not uniquely identify the movable entity;

calculating, by the first sensor device, a tracking value representative of the derivative identifying data; and

sending, by the first sensor device, a message from the first sensor device to a central server for determining the position of the movable entity, the message comprising the tracking value and an identifier of the first sensor device.

2. The computer-implemented method according to claim 1, wherein the tracking value is a hash value calculated from the derivative identifying data based on a hash function applied to the derivative identifying data.

3. The computer-implemented method according to claim 1, further comprising:

synchronizing among a plurality of sensor devices, a first synchronized data value for a current time period, wherein during other time periods other synchronized data values are synchronized among the plurality of sensor devices, and wherein calculating the derivative identifying data by applying the modifying function on the identifying data further comprises providing the identifying data as a first input value to the modifying function and the first synchronized data value as a second input to the modifying function such that the modifying function modifies the first identifying data based on a function of the first synchronized data value.

4. The computer-implemented method according to claim 3, wherein the first synchronized data value is a first random data value and wherein synchronizing the first synchronized data value comprises executing a random function based on a time value, corresponding to a current time slot, on the first sensor device.

5. The computer-implemented method according to claim 3, wherein synchronizing the first synchronized data value comprises:

generating, by a central server, the first synchronized data value as a random data value; and

sending, by the central server, the first synchronized data value to each of the sensor devices in the plurality of sensor devices via a network.

6. The computer-implemented method according to claim 1, wherein the generated set of data is uniquely identifiable data that uniquely identifies the movable entity.

7. The computer-implemented method according to claim 1, further comprising:

determining, by the first sensor device, one or more parameter values for the generated set of data, the one or more parameter values being selected, in any combination, from a group of parameter values comprising;

a timestamp information being indicative of a moment in time when the set of data was generated;

a parameter value of the movable entity, the parameter value being automatically derived by the first sensor device by evaluating the generated set of data; and

a property of the first sensor device, wherein the sent message in addition comprises the one or more determined parameter values.

8. The computer-implemented method according to claim 1, wherein the movable entity is a human being, the sensor devices are cameras, the generated set of data is an image taken of the human being by the first sensor device, and wherein the identifying data is a face-print or thermal print of the human being.

9. The computer-implemented method according to claim 1, wherein the movable entity is a human being, the sensor devices are microphones, the generated set of data is a voice recording from the human being recorded by the first sensor device, and wherein the identifying data is a voice profile of the human being.

10. The computer-implemented method according to claim 1, wherein the movable entity is a vehicle, the one or more sensor devices are cameras or radar sensors, the generated set of data is image data of the vehicle'"'"'s number plate, and the number specified on the vehicle'"'"'s number plate is the identifying data.

11. A sensor device for tracking at least one movable entity, the sensor device comprising a processor and a memory coupled to the processor, wherein the memory comprises program logic executed by the processor to cause the processor to:

generate a set of data based on sensing of one or more attributes of a movable entity present in the physical space;

calculate identifying data from the generated set of data, wherein the identifying data is a reduced size set of data relative to the generated set of data and is directed to identifying characteristics of the movable entity within the generated set of data;

calculate derivative identifying data by applying a modifying function on the identifying data, wherein the derivative identifying data is anonymized data, calculated from the identifying data, that does not uniquely identify the movable entity;

calculate a tracking value representative of the derivative identifying data; and

send a message from the first sensor device to a central server for determining the position of the movable entity, the message comprising the tracking value and an identifier of the first sensor device.

12. The sensor device according to claim 11, wherein the tracking value is a hash value calculated from the derivative identifying data based on a hash function applied to the derivative identifying data.

13. The sensor device according to claim 11, wherein the program logic further causes the processor to:

synchronize among a plurality of sensor devices, a first synchronized data value for a current time period, wherein during other time periods other synchronized data values are synchronized among the plurality of sensor devices, and wherein calculating the derivative identifying data by applying the modifying function on the identifying data further comprises providing the identifying data as a first input value to the modifying function and the first synchronized data value as a second input to the modifying function such that the modifying function modifies the first identifying data based on a function of the first synchronized data value.

14. The sensor device according to claim 13, wherein the first synchronized data value is a first random data value and wherein synchronizing the first synchronized data value comprises executing a random function based on a time value, corresponding to a current time slot, on the first sensor device.

15. The sensor device according to claim 11, wherein the generated set of data is uniquely identifiable data that uniquely identifies the movable entity.

16. The sensor device according to claim 11, wherein the program logic further causes the processor to:

determine one or more parameter values for the generated set of data, the one or more parameter values being selected, in any combination, from a group of parameter values comprising;

a timestamp information being indicative of a moment in time when the set of data was generated;

a parameter value of the movable entity, the parameter value being automatically derived by the sensor device by evaluating the generated set of data; and

a property of the sensor device, wherein the sent message in addition comprises the one or more determined parameter values.

17. The sensor device according to claim 11, wherein the movable entity is a human being, the sensor devices are cameras, the generated set of data is an image taken of the human being by the first sensor device, and wherein the identifying data is a face-print or thermal print of the human being.

18. The sensor device according to claim 11, wherein the movable entity is a human being, the sensor devices are microphones, the generated set of data is a voice recording from the human being recorded by the first sensor device, and wherein the identifying data is a voice profile of the human being.

19. The sensor device according to claim 11, wherein the movable entity is a vehicle, the one or more sensor devices are cameras or radar sensors, the generated set of data is image data of the vehicle'"'"'s number plate, and the number specified on the vehicle'"'"'s number plate is the identifying data.

20. A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

generate a set of data based on sensing of one or more attributes of a movable entity present in the physical space;

calculate identifying data from the generated set of data, wherein the identifying data is a reduced size set of data relative to the generated set of data and is directed to identifying characteristics of the movable entity within the generated set of data;

calculate derivative identifying data by applying a modifying function on the identifying data, wherein the derivative identifying data is anonymized data, calculated from the identifying data, that does not uniquely identify the movable entity;

calculate a tracking value representative of the derivative identifying data; and

send a message from the first sensor device to a central server for determining the position of the movable entity, the message comprising the tracking value and an identifier of the first sensor device.