System and method for confirming location using supplemental sensor and/or location data

• US 9,367,676 B2
• Filed: 12/31/2013
• Issued: 06/14/2016
• Est. Priority Date: 03/22/2013
• Status: Active Grant

• Current Assignee: Nok Nok Labs Incorporated
• Original Assignee: Nok Nok Labs Incorporated
• Inventors: Wilson, Brendon J.
• Primary Examiner(s): Hirl, Joseph P
• Assistant Examiner(s): Gundry, Stephen
• Application Number:
  US14/145,607
• Publication Number:
  US 20140289822A1
• Time in Patent Office: 896 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 2221/2115 Third party
• CPC Class Codes: G06Q 20/204 comprising interface for record bearing medium or carrier for electronic funds transfer or payment credit
• CPC Class Codes: G06Q 20/3224 Transactions dependent on location of M-devices
• CPC Class Codes: G06Q 20/3274 using a pictured code, e.g. barcode or QR-code, being displayed on the M-device
• CPC Class Codes: G06Q 20/3278 RFID or NFC payments by means of M-devices
• CPC Class Codes: G06Q 20/4012 Verifying personal identification numbers [PIN]
• CPC Class Codes: G06Q 20/40145 Biometric identity checks
• CPC Class Codes: G06Q 20/42 Confirmation, e.g. check or permission by the legal debtor of payment
• CPC Class Codes: G06Q 20/425 using two different networks, one for transaction and one for security confirmation
• CPC Class Codes: G07F 19/20 Automatic teller machines [ATMs]
• CPC Class Codes: H04L 2209/805 Lightweight hardware, e.g. radio-frequency identification [RFID] or sensor
• CPC Class Codes: H04L 2463/102 applying security measure for e-commerce data processing systems or methods, specially adapted for e-commerce G06Q30/00
• CPC Class Codes: H04L 63/0492 by using a location-limited connection, e.g. near-field communication or limited proximity of entities
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 63/083 using passwords cryptographic mechanisms or cryptographic arrangements for entity authentication using a predetermined code H04L9/3226
• CPC Class Codes: H04L 63/0861 using biometrical features, e.g. fingerprint, retina-scan cryptographic mechanisms or cryptographic arrangements for entity authentication using biological data H04L9/3231
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 9/0819 Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s) network architectures or network communication protocols for key distribution in a packet data network H04L63/062
• CPC Class Codes: H04L 9/0822 : using key encryption key
• CPC Class Codes: H04L 9/0841 : involving Diffie-Hellman or related key agreement protocols
• CPC Class Codes: H04L 9/3231 : Biological data, e.g. fingerprint, voice or retina network architectures or network communication protocols for supporting authentication of entities using biometrical features in a packet data network H04L63/0861
• CPC Class Codes: H04L 9/3247 : involving digital signatures
• CPC Class Codes: H04L 9/3297 : involving time stamps, e.g. generation of time stamps
• CPC Class Codes: H04W 12/06 : Authentication
• CPC Class Codes: H04W 12/065 : Continuous authentication

Abstract

A system, apparatus, method, and machine readable medium are described for performing authentication using environmental data. For example, one embodiment of a method comprises: collecting environmental sensor data from one or more sensors of a client device; using a geographical location reported by the device to collect supplemental data for the location; comparing the environmental sensor data with the supplemental data to arrive at a correlation score; and responsively selecting one or more authentication techniques for authenticating a user of the client device based on the correlation score.

Claims

1. A method for user authentication implemented on a client device comprising a memory for storing program code and a processor for processing the program code to cause operations to be performed on the client device comprising:

collecting environmental sensor data from one or more sensors configured on the client device;

determining a current geographical location of the client device from one or more location sensors configured on the client device;

using the geographical location to identify supplemental data associated with the location;

comparing the environmental sensor data with the supplemental data to arrive at a correlation score indicating a level of correlation between the environmental sensor data and the supplemental data, the operation of comparing performed through execution of the program code by the processor; and

responsively selecting one or more authentication techniques for authenticating a user of the client device based on the correlation score and based on an authentication policy associated with a relying party, the operation of responsively selecting performed through execution of the program code by the processor, the authentication techniques including biometric authentication techniques implemented using one or more biometric sensors communicatively coupled to the client device and configured to sense biometric data provided by the user, wherein responsively selecting further comprises selecting a first set of one or more authentication techniques required to meet a first specified assurance threshold for correlation scores below a specified correlation threshold and selecting a second set of one or more authentication techniques required to meet a second specified assurance threshold for correlation scores above the specified correlation threshold, the first specified assurance threshold being greater than the second specified assurance threshold;

wherein the supplemental data is collected from one or more network servers known to include accurate environmental data for the reported location.

2. The method as in claim 1 wherein the environmental sensor data comprises a temperature reading, a humidity reading, or a pressure reading.

3. The method as in claim 2 wherein the supplemental data comprises a current temperature, humidity, or pressure associated with the location.

4. The method as in claim 3 wherein the supplemental data is collected from one or more other client devices known to be at the reported location or within a specified distance from the reported location.

5. The method as in claim 3 wherein the supplemental data includes historical data related to the location.

6. The method as in claim 1 wherein comparing comprises determining differences between the supplemental data and the environmental sensor data.

7. The method as in claim 6 wherein the greater the differences, the lower the correlation score and the smaller the differences, the greater the correlation score.

8. The method as in claim 1 wherein for correlation scores below a specified threshold, one or more explicit user authentication techniques are required.

9. The method as in claim 8 wherein the explicit user authentication techniques include manual user personal identification number (PIN) entry, fingerprint authentication, voice authentication, facial authentication, or retinal scan authentication.

10. The method as in claim 1 wherein for correlation scores above a specified threshold, one or more non-intrusive user authentication techniques are implemented.

11. The method as in claim 10 wherein one of the non-intrusive techniques comprises collecting data from one or more additional sensors to detect known behavior of the user.

12. The method as in claim 11 wherein the known behavior of the end user comprises the user'"'"'s gait.

13. A client device comprising a memory for storing program code, one or more sensors, and a processor for processing the program code for user authentication comprising:

one or more environmental sensors configured on the client device providing environmental sensor data;

one or more location sensors configured on the client device to determine a current geographical location of the client device;

a supplemental data collection module comprising program code executed by the processor to use the geographical location reported by the one or more location sensors to identify supplemental data associated with the location;

the supplemental data correlation module to compare the environmental sensor data with the supplemental data to arrive at a correlation score indicating a level of correlation between the environmental sensor data and the supplemental data; and

an authentication policy module comprising program code executed by the processor to responsively select one or more authentication techniques for authenticating a user of the client device based on the correlation score and based on an authentication policy associated with a relying party; and

one or more biometric sensors communicatively coupled to the client device and configured to sense biometric data provided by the user;

wherein the authentication techniques include biometric authentication techniques implemented using the one or more biometric sensors communicatively coupled to the client device and configured to sense biometric data provided by the user, wherein to responsively select the authentication techniques, the authentication policy module is configured to select a first set of one or more authentication techniques required to meet a first specified assurance threshold for correlation scores below a specified correlation threshold and to select a second set of one or more authentication techniques required to meet a second specified assurance threshold for correlation scores above the specified correlation threshold, the first specified assurance threshold being greater than the second specified assurance threshold;

wherein the supplemental data is collected from one or more network servers known to include accurate environmental data for the reported location.

14. The client device as in claim 13 wherein the environmental sensors comprise a temperature sensor, a humidity sensor or a pressure sensor and wherein the sensor data comprises a temperature reading, a humidity reading, and/or a pressure reading.

15. The client device as in claim 14 wherein the supplemental data comprises a current temperature, humidity, or pressure associated with the location.

16. The client device as in claim 15 wherein the supplemental data is collected from one or more other client devices known to be at the reported location or within a specified distance from the reported location.

17. The client device as in claim 15 wherein the supplemental data includes historical data related to the location.

18. The client device as in claim 13 wherein comparing comprises determining differences between the supplemental data and the environmental sensor data.

19. The system as in claim 18 wherein the greater the differences, the lower the correlation score and the smaller the differences, the greater the correlation score.

20. The client device as in claim 13 wherein for correlation scores below a specified threshold, one or more explicit user authentication techniques are required.

21. The client device as in claim 20 wherein the explicit user authentication techniques include manual user personal identification number (PIN) entry, fingerprint authentication, voice authentication, facial authentication, or retinal scan authentication.

22. The client device as in claim 13 wherein for correlation scores above a specified threshold, one or more non-intrusive user authentication techniques are implemented.

23. The client device as in claim 22 wherein one of the non-intrusive techniques comprises collecting data from one or more additional sensors to detect known behavior of the user.

24. The client device as in claim 23 wherein the known behavior of the end user comprises the user'"'"'s gait.