Tracking entities by means of hash values

• US 9,600,443 B2
• Filed: 01/14/2013
• Issued: 03/21/2017
• Est. Priority Date: 01/30/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Albano, Gherardo, De Judicibus, Dario
• Primary Examiner(s): Breene, John
• Assistant Examiner(s): BLOSS, STEPHANIE E
• Application Number:
  US13/740,420
• Publication Number:
  US 20130197859A1
• Time in Patent Office: 1,527 Days
• Field of Search: 702/150
• US Class Current: 1/1
• 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:

a) synchronizing, with one or more other sensor devices, a first data value corresponding to a first time slot associated with a current time, the first time slot being one of a series of time slots shared by all the sensor devices;

b) generating a set of data based on sensing of one or more attributes of the movable entity, wherein the set of data is uniquely identifiable data that uniquely identifies the movable entity;

c) calculating identifying data from the generated set of data for identifying the movable entity based on the identifying 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;

d) calculating derivative identifying data by applying a modifying function on the identifying data, the modifying function using the identifying data as first input value and using the first data value as a second input value for modifying the identifying data based on the first data value, wherein the derivative identifying data is anonymized data which does not uniquely identify the movable entity;

e) calculating a first hash value at least by applying a hash function to the derivative identifying data; and

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

2. The computer-implemented method according to claim 1, the method further comprising encrypting the message before sending the message to the central server.

3. The computer-implemented method according to claim 1, wherein the hash function is operable to calculate, for any pair of two similar input values, a pair of similar output values, whereby a degree of similarity of said pair of input values correlates with a degree of similarity of said pair of output values.

4. The computer-implemented method according to claim 1, the method further comprising, for k second time slots preceding the first time slot in the series of time slots, k being an integer larger than 1, the following:

determining one of the k second time slots, the determined second time slot preceding a current time slot;

providing other first data, the other first data being particular to the determined second time slot and being shared between the sensor devices;

calculating a second derivative identifying data by applying a modifying function on the identifying data, the modifying function using the identifying data as first input value and using the provided other first data as a second input value for modifying the identifying data;

calculating by said first sensor device a second hash value at least by applying the hash function to the second derivative identifying data; and

tracking a motion of the movable entity across the k second time slots and the first time slot by comparing the first hash value and each of the k second hash values.

5. The computer-implemented method according to claim 1, wherein the first data is first random data and wherein synchronizing the first data comprises executing a random function based on a time value corresponding to the first time slot, on the first sensor device.

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

g) repeating the operations b) to e) n times, n being an integer larger than 2 during the first time slot, thereby receiving n sets of data from the movable entity and calculating for each of said n received sets of data and for the first time slot a respective first hash value, wherein for the calculation of each of said n first hash values the respectively generated set of data was used as input;

h) storing each of the n first hash values on a storage medium of the first sensor device;

i) determining by said first sensor device that the first time slot has expired; and

j) upon said determination, executing operation f), wherein the message comprises said n first hash values.

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

repeating the steps a) to f) for each of j time slots, j being an integer larger than 3, thereby receiving one or more sets of data for each of the j time slots and calculating one or more further hash values for each of said j time slots, wherein for the calculation of each further hash value of one of said j time slots the same first data is used, wherein the one or more further hash values are stored in a storage medium of the first sensor device; and

after having sent the message created for each one of the j time slots, deleting all hash values having been calculated for the m-last time slot and any time slot preceding said m-last time slot, wherein m is an integer larger than 0 and smaller than j.

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

determining, by said 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, said parameter 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.

9. The computer-implemented method according to claim 1, wherein synchronizing the first data comprises:

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

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

10. 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;

or

the movable entity is a human being, the sensor devices are microphones, the generated set of data is a voice record from the human being recorded by the first sensor device, and wherein the identifying data is a voice profile of the human being.

11. The computer-implemented method of 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.

12. A sensor device for tracking at least one movable entity, the sensor device comprising:

a clock for determining a current time, the current time lying within a first time slot, the first time slot being one of a series of time slots, the clock being adapted for synchronizing the clock with clocks of one or more other sensor devices;

a controller module being adapted for;

synchronizing, with one or more other sensor devices, a first data value corresponding to the first time slot;

a first interface being adapted for generating a set of data based on sensing one or more attributes of the movable entity, wherein the set of data is uniquely identifiable data that uniquely identifies the movable entity;

a calculation unit, the calculation unit being adapted for;

calculating identifying data from the generated set of data for identifying the movable entity based on the identifying 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 derivative identifying data by applying a modifying function on the identifying data, the modifying function using the identifying data as first input value and using the first data value as a second input value for modifying the identifying data based on the first data value, wherein the derivative identifying data is anonymized data which does not uniquely identify the movable entity; and

calculating a first hash value at least by applying a hash function to the derivative identifying data; and

a second interface being adapted for sending a message from the first sensor device to a central server for indicating the position of the movable entity, the message comprising the hash value and an identifier of the first sensor device.

13. 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:

a) determine synchronizing, with one or more other sensor devices, a first data value corresponding to a first time slot associated with a current time, the first time slot being one of a series of time slots shared by all the sensor devices;

b) generate a set of data based on sensing of one or more attributes of the movable entity, wherein the set of data is uniquely identifiable data that uniquely identifies the movable entity;

c) calculate identifying data from the generated set of data for identifying the movable entity based on the identifying 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;

d) calculate derivative identifying data by applying a modifying function on the identifying data, the modifying function using the identifying data as first input value and using the first data value as a second input value for modifying the identifying data based on the first data value, wherein the derivative identifying data is anonymized data which does not uniquely identify the movable entity;

e) calculate a first hash value at least by applying a hash function to the derivative identifying data; and

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

14. The computer program product according to claim 13, wherein the computer readable program further causes the computing device to encrypt the message before sending the message to the central server.

15. The computer program product according to claim 13, wherein the hash function is operable to calculate, for any pair of two similar input values, a pair of similar output values, whereby a degree of similarity of said pair of input values correlates with a degree of similarity of said pair of output values.

16. The computer program product according to claim 13, wherein the computer readable program further causes the computing device, for k second time slots preceding the first time slot in the series of time slots, k being an integer larger than 1, to perform the following:

determine one of the k second time slots, the determined second time slot directly preceding a current time slot;

provide other first data, the other first data being particular to the determined second time slot and being shared between the sensor devices;

calculate a second derivative identifying data by applying a modifying function on the identifying data, the modifying function using the identifying data as first input value and using the provided other first data as a second input value for modifying the identifying data;

calculate by said first sensor device a second hash value at least by applying the hash function to the second derivative identifying data; and

track a motion of the movable entity across the k second time slots and the first time slot by comparing the first hash value and each of the k second hash values.

17. The computer program product according to claim 13, wherein the first data is first random data and wherein synchronizing the first data value comprises executing a random function based on a time value corresponding to the first time slot, on the first sensor device.

18. The computer program product according to claim 13, wherein the computer readable program further causes the computing device to:

g) repeat the operations b) to e) n times, n being an integer larger than 2 during the first time slot, thereby receiving n sets of data from the movable entity and calculating for each of said n received sets of data and for the first time slot a respective first hash value, wherein for the calculation of each of said n first hash values the respectively generated set of data was used as input;

h) store each of the n first hash values on a storage medium of the first sensor device;

i) determine by said first sensor device that the first time slot has expired; and

j) upon said determination, executing operation g), wherein the message comprises said n first hash values.

19. The computer program product of claim 13, 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 system comprising:

a centralized server;

a plurality of sensors coupled to the centralized server by one or more data networks, wherein at least one sensor in the plurality of sensors operates to;

synchronize, with one or more other sensor devices, a first data value corresponding to a first time slot associated with a current time, the first time slot being one of a series of time slots shared by all the sensor devices;

generate a set of data based on sensing of one or more attributes of the movable entity, wherein the set of data is uniquely identifiable data that uniquely identifies the movable entity;

calculate identifying data from the generated set of data for identifying the movable entity based on the identifying 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, the modifying function using the identifying data as first input value and using the first data value as a second input value for modifying the identifying data based on the first data value, wherein the derivative identifying data is anonymized data which does not uniquely identify the movable entity;

calculate a first hash value at least by applying a hash function to 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 hash value and an identifier of the first sensor device.

21. The computer system of claim 20, wherein the centralized server operates to track a motion of the movable entity by comparing hash values calculated for time slots in the series of time slots.