Aggregation of asynchronous trust outcomes in a mobile device

• US 20150373050A1
• Filed: 06/23/2014
• Published: 12/24/2015
• Est. Priority Date: 06/23/2014
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Dayan, Tal, Ben Ari, Maya, Gibbs, Tanton Holt, Ofir, Ido, Civelli, Jay Pierre, Keely, Brandon, Miller, James Brooks, Prins, Christiaan, Sun, Zheng, Zheng, Ning, Fernquist, Jennifer, Xiang, Rongjing, McMahan, Hugh Brendan
• Granted Patent: US 10,148,692 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04W 12/06 Authentication
• CPC Class Codes: H04W 12/30 Security of mobile devices; Security of mobile applications
• CPC Class Codes: H04W 12/63 Location-dependent; Proximity-dependent
• CPC Class Codes: H04W 12/65 Environment-dependent, e.g. using captured environmental data
• CPC Class Codes: H04W 88/02 Terminal devices

Abstract

Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Claims

1. A computer-implemented method performed by a data processing apparatus, the method comprising:

receiving one or more signals from one or more sensors;

determining at least two trust levels from the one or more signals, wherein each trust level is determined independently of any other trust level, and wherein each trust level is determined based on applying to the one or more signals one or more of;

heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems;

determining an aggregated trust outcome by aggregating the at least two trust levels, wherein aggregating the at least two trust levels further comprises applying one or more of heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems to the at least two trust levels; and

sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

2. The computer-implemented method of claim 1, wherein determining one of the at least two trust levels further comprises applying data from at least one state wherein the data comprises one or more of a historical trust level and a historical value for one of the one or more signals used to determine the one of the at least two trust levels.

3. The computer-implemented method of claim 1, wherein determining the aggregated trust outcome further comprises applying at least one configuration setting.

4. The computer-implemented method of claim 1, wherein determining the aggregated trust outcome further comprises applying data from a state, wherein the data comprises one or more of a historical trust level and a historical aggregated trust outcome.

5. The computer-implemented method of claim 1, wherein the aggregated trust outcome is either a global aggregated trust outcome or a granular aggregated trust outcome.

6. The computer-implemented method of claim 5, wherein a granular aggregated trust outcome is implemented by the enabling, disabling, or relaxing of security measures associated with the granular aggregated trust outcome and cannot affect security measures not associated with the granular aggregated trust outcome.

7. The computer-implemented method of claim 1, wherein the aggregated trust outcome indicates a confidence level based on aggregating trust levels that a user of a mobile computing device is an authorized user of the mobile computing device.

8. The computer-implemented method of claim 1, wherein one of the at least two trust levels indicates a confidence level based on analyzing received signals that a user of a mobile computing device is an authorized user of the mobile computing device.

9. The computer-implemented method of claim 1, wherein the security measure is a request for credentials to unlock a mobile computing device.

10. A computer-implemented method performed by a data processing apparatus, the method comprising:

receiving a plurality of signals from a plurality of sensors, wherein the plurality of sensors comprises hardware and software sensors of a computing device;

determining a plurality of trust levels from the plurality of signals, wherein each of the plurality of trust levels is determined independently from the rest of the plurality of trust levels;

aggregating the plurality of trust levels to determine an aggregated trust outcome using one or more of heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems; and

sending the aggregated trust outcome.

11. The computer-implemented method of claim 10, wherein each of the plurality of trust levels is determined by a trustlet.

12. The computer-implemented method of claim 10, wherein the aggregated trust outcome is determined by a trust aggregator.

13. The computer-implemented method of claim 10, wherein each of the plurality of trust levels is a confidence level that a mobile computing device is either being used by an authorized user or is in a secure environment.

14. The computer-implemented method of claim 10, wherein the aggregated trust outcome is a confidence level that a mobile computing device is either being used by an authorized user or is in a secure environment.

15. The computer-implemented method of claim 11, wherein at least one trustlet determines one of the plurality of trust levels based in part on a state associated with the trustlet.

16. The computer-implemented method of claim 12, wherein the trust aggregator determines the aggregated trust outcome based in part on a state of the trust aggregator.

17. The computer-implemented method of claim 12, wherein the trust aggregator determines the aggregated trust outcome based in part on configuration settings for the trust aggregator.

18. The computer-implemented method of claim 10, further comprising enabling at least one security measure of a mobile computing device when the aggregated trust outcome is below a threshold.

19. The computer-implemented method of claim 10, further comprising disabling at least on security measure of a mobile computing device when the aggregated trust outcome is above a threshold.

20. A computer-implemented system for aggregation of asynchronous trust outcomes comprising:

a storage comprising configuration settings;

one or more sensors, each sensor adapted to generate at least one signal;

at least two trustlets, each of the at least two trustlets adapted to receive at least signal from the one or more sensors and determine a trust level; and

a trust aggregator adapted to aggregate the trust levels, determine an aggregated trust outcome, and send the aggregated trust outcome.

21. The computer-implemented system of claim 20, further comprising a trust consumer adapted to receive the aggregated trust consumer and disable, enable, or relax at least one security measure based on the aggregated trust outcome.

22. The computer-implemented system of claim 20, wherein at least one of the trustlets is further adapted to determine a trust level using one or more of heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems;

23. The computer-implemented system of claim 20, wherein the trust aggregator is further adapted to aggregate the trust levels and determine the aggregated trust outcome using one or more of heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems.

24. The computer-implemented system of claim 20, wherein the aggregated trust outcome indicates a level of confidence that a mobile computing device is being used by an authorized user.

25. A system comprising:

one or more computers and one or more storage devices storing instructions which are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

receiving one or more signals from one or more sensors;

determining at least two trust levels from the one or more signals, wherein each trust level is determined independently of any other trust level, and wherein each trust level is determined based on applying to the one or more signals one or more of;

heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems;

determining an aggregated trust outcome by aggregating the at least two trust levels, wherein aggregating the at least two trust levels further comprises applying one or more of heuristics, mathematical optimization, decisions trees, machine learning systems, and artificial intelligence systems to the at least two trust levels; and

sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.