SYSTEM, METHOD AND APPARATUS FOR SECURE MULTIPARTY LOCATION BASED SERVICES

• US 20090030778A1
• Filed: 07/23/2008
• Published: 01/29/2009
• Est. Priority Date: 07/23/2007
• Status: Abandoned Application

• Current Assignee: Motivepath Incorporated, Moviepath, Inc.
• Original Assignee: Motivepath Incorporated
• Inventors: Ficcaglia, Robert, Zapata, Daniel
• Application Number:
  US12/178,592
• Publication Number:
  US 20090030778A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

A computer system implements a method to provide secure multiparty location based services. A user input is received from a user device of a user. The user input contains user location information. Based on the location information, a model is retrieved. The model is a constraint satisfaction problem defined by a set of variables and mapping functions. The set of variables and mapping functions are into multiple shares. Each share is distributed to one of agents in finding a solution to the constraint satisfaction problem. Once a solution is computed, a demographic profile is predicted based on the solution. The solution does not contain the user location information.

Claims

1. A method, comprising:

receiving a user input from a user device of a user, wherein the user input contains user location information;

constructing and retrieving a model based on the user location information, wherein the model is a constraint satisfaction problem defined by a set of variables and mapping functions;

dividing the set of variables and mapping functions into a plurality of shares;

distributing the plurality of shares to a plurality of agents, wherein each of the plurality of agents participates in finding a solution to the constraint satisfaction problem;

predicting a demographic profile based on the solution, wherein the solution does not contain the user location information.

2. The method as recited in claim 1, further comprising:

predicting a set of metadata based on the solution, wherein the solution does not contain the user location information.

3. The method as recited in claim 1, further comprising:

predicting a geometric or geographic result based on the solution, wherein the solution does not contain the user location information.

4. The method as recited in claim 1, further comprising:

providing targeted commercial services to the user device based on the predicted demographic profile.

5. The method as recited in claim 1, wherein the set of variables and mapping functions for the constraint satisfaction problem is constructed by using a machine learning algorithm.

6. The method as recited in claim 5, wherein the machine learning algorithm is a Support Vector Machine (SVM), a Fuzzy Neural Network (FNN), a Bayesian Classifier, a Genetic Algorithm, a probabilistic model, or a statistical model.

7. The method as recited in claim 1, wherein the set of variables and mapping functions for the constraint satisfaction problem does not reveal the user location information to the plurality of agents.

8. The method as recited in claim 1, wherein the demographic profile is associated with the user.

9. The method as recited in claim 1, wherein the demographic profile is associated with an aggregation of multiple users.

10. The method as recited in claim 1, wherein the model is associated with the user.

11. The method as recited in claim 1, wherein the model is associated with an aggregation of multiple users.

12. The method as recited in claim 1, wherein the plurality of shares is shared securely to the plurality of agents without disclosing the user location information.

13. The method as recited in claim 12, wherein the plurality of shares is shared securely by means of a Blakely or Shamir sharing scheme.

14. The method as recited in claim 1, further comprising:

providing a zero-knowledge proof for validating information relevant to the user location information.

15. The method as recited in claim 1, further comprising:

providing a zero-knowledge proof for validating each of the plurality of agents.

16. The method as recited in claim 1, further comprising:

providing a zero-knowledge proof for validating the solution.

17. The method as recited in claim 1, wherein the distributing of the plurality of shares to a plurality of agents further comprises:

optionally performing a shuffling operating across the plurality of agents; and

upon finding intermediate solutions by the plurality of agents to the constraint satisfaction problem, optionally performing an unshuffling operation on the intermediate solutions across the plurality of agents.

18. The method as recited in claim 1, wherein the method is embodied in a machine-readable medium as a set of instructions which, when executed by a processor, cause the processor to perform the method.

19. A method, comprising:

registering to a secured multiparty location based system;

participating in a distributed computing of a Distributed Constraint Satisfaction Problem (DistCSP), wherein the DistCSP is obtained based on a user request, and the user request contains location information of a user;

receiving a share of variables and mapping functions associated with the DistCSP;

computing a solution for the DistCSP based on the share of variables and mapping functions; and

delivering the solution to the secured multiparty location based system, wherein the solution is utilized in generating a demographic profile of the user.

20. The method as recited in claim 19, further comprising

validating the location information of the user without accessing the location information by using a zero-knowledge proof.

21. The method as recited in claim 19, wherein the DistCSP, the solution, and the demographic profile do not contain the location information.

22. The method as recited in claim 19, wherein the method is embodied in a machine-readable medium as a set of instructions which, when executed by a processor, cause the processor to perform the method.

23. A system, comprising:

a machine learning solver server for generating a Distributed Constraint Satisfaction Problem (DistCSP) with a machine learning algorithm, and for providing the DistCSP based on location information obtained from a user device; and

a Distributed Constraint Satisfaction Solver Master Server (DCSSMS) for secure multiparty computing a solution to the DistCSP and generating a demographic profile of the user, wherein the DistCSP, the solution, and the demographic profile do not contain the location information.

24. The system as recited in claim 23, further comprising:

a plurality of service providers for participating in the secure multiparty computing of the DistCSP, wherein the location information is not disclosed to any one of the plurality of service providers.

25. The system as recited in claim 23, wherein each of the plurality of service providers is capable of validating the location information of the user without accessing the location information by using a zero-knowledge proof.