System For Privacy-Preserving Monetization Of Big Data And Method For Using The Same

• US 20160323102A1
• Filed: 04/28/2015
• Published: 11/03/2016
• Est. Priority Date: 04/28/2015
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Freudiger, Julien, Rane, Shantanu, Brito, Alejandro E., Uzun, Ersin
• Granted Patent: US 11,088,834 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: H04L 2209/16 Obfuscation or hiding, e.g. involving white box
• CPC Class Codes: H04L 2209/24 Key scheduling, i.e. generating round keys or sub-keys for block encryption
• CPC Class Codes: H04L 9/0841 involving Diffie-Hellman or related key agreement protocols
• CPC Class Codes: H04L 9/0869 involving random numbers or seeds

Abstract

The current invention provides a system and method for Data Owners to share with Data Seekers extracted insights from the Big Data, instead of raw data or anonymized raw data, thus reducing or eliminating privacy concerns on the data owned by the Data Owners. An Oblivious Pseudo Random Function (OPRF) is used, with operations using OPRFs occur over encrypted data, thus Data Owners learn only the primary object from Data Seeker and nothing else about the remainder of Data Owners'"'"' data. Similarly, Data Seeker learns a list of associated secondary objects and nothing else about Data Owners'"'"' data. The extent of sharing can be limited using a predefined threshold depending how much private information Data Owner is willing to share or Data Seeker is willing to pay.

Claims

1. A computer-implemented method for privacy-preserving insight sharing, comprising:

receiving, by a data owner, an identity of a primary object and an obfuscated list of secondary objects from a requesting party;

modifying, by the data owner, the obfuscated list of the secondary objects to create a modified obfuscated list of the secondary objects;

transmitting, from the data owner to the requesting party, the modified obfuscated list of the secondary objects;

computing at least one association relationship between the primary object and associated objects to obtain a list of insights;

selecting, by the data owner, the list of the insights, to obtain a selected list of the insights;

obfuscating, by the data owner, the selected list of the insights, to obtain an obfuscated selected list of the insights;

transmitting the obfuscated selected list of the insights to the requesting party;

providing an intersection between the modified obfuscated list of the secondary objects and the obfuscated selected list of the insights by enabling the requesting party to compare the modified obfuscated list of the secondary objects and the obfuscated selected list of the insights.

2. A method according to claim 1, wherein the secondary objects and the selected list of the insights are obfuscated via an Oblivious Pseudo Random Function.

3. A method according to claim 2, wherein the Oblivious Pseudo Random Function is based on at least one of RSA algorithm, Diffie-Hellman algorithm, or a hashing algorithm.

4. A method according to claim 1, further comprising the steps of:

obfuscating the list of the secondary objects by computing {H(x₁)·

r^e, H(x₂)·

r^e, . . . , H(x_n)·

r^e};

modifying the obfuscated list of the secondary objects by computing {(H(x₁)·

(H(x₂)·

r^e)^d, . . . , (H(x_n)·

r^e)^d}; and

transmitting the obfuscated selected list of the insights to the requesting party in the form of {H(x₁)^d, H(x₂)^d, . . . , H(x_m)^d},

wherein H(x) is cryptographic hash function, e is the public key of a RSA algorithm, d is a the private key of the RSA algorithm, r is a random integer, the x₁, x₂ . . . , x_n are the secondary objects, and the x₁, x₂ . . . , x_m constitute the list of the insights.

5. A method according to claim 1, further comprising:

receiving a payment from the requesting party in exchange for the obfuscated selected list of the insights.

6. A method according to claim 1, further comprising the step of:

selecting the list of the insights based on at least one of the factors chosen from the group of a number of the insights, a specification from the receiving party, or a strength of the association relationship between the primary object and the associated objects.

7. A method according to claim 1, wherein the secondary objects comprise a mathematical description of at least one of goods, services, activities, and events.

8. A method according to claim 1, further comprising:

computing the insights using a machine-learning algorithm.

9. A method according to claim 1, further comprising:

computing the insights by the steps of;

maintaining an inventory of goods;

obtaining shopping data on the inventory of the goods; and

computing an association relationship between purchasing one good and purchasing another good based on the shopping data.

10. A method according to claim 1, wherein the association is positive or negative.

11. A non-transitory computer readable storage medium storing code for executing on a computer system to perform the method according to claim 1.

12. A computer-implemented system for privacy-preserving insight sharing, comprising:

a receiving module configured to receive an identity of a primary object and an obfuscated list of secondary objects from a requesting party;

an encryption module configured to modify the obfuscated list of the secondary objects to create a modified obfuscated list of the secondary objects;

a sharing module configured to transmit to the requesting party the modified obfuscated list of the secondary objects;

an insight computing module configured to compute at least one association relationship between the primary object and associated objects to obtain a list of insights;

a threshold module configured to select the list of the insights to obtain a selected list of the insights;

the encryption module further configured to obfuscate the selected list of the insights to obtain an obfuscated selected list of the insights;

the sharing module further configured to transmit the obfuscated selected list of the insights to the requesting party;

a comparison module configured to provide an intersection between the modified obfuscated list of the secondary objects and the obfuscated selected list of the insights through comparing the modified obfuscated list of the secondary objects and the obfuscated selected list of the insights.

13. A system according to claim 12, wherein the encryption module further comprises a submodule configured to employ an Oblivious Pseudo Random Function.

14. A system according to claim 13, wherein the Oblivious Pseudo Random Function is based on at least one of RSA algorithm, Diffie-Hellman algorithm, and a hashing algorithm.

15. A system according to claim 12, further comprising:

the encryption module further configured to obfuscate the list of the secondary objects by computing {H(x₁)·

r_e, H(x₂)·

r_e, . . . , H(x_n)·

r^e}; and

to modify the obfuscated list of the secondary objects by computing {(H(x₁)˜

r^e)^d, (H(x₂)·

r^e)^d, . . . , (H(x_n)·

r^e)^d}; and

the sharing module further configured to transmit the obfuscated selected list of the insights to the requesting party in the form of {H(x₁)^d, H(x₂)^d, . . . , H(x_m)^d},

wherein H(x) is cryptographic hash function, e is the public key of a RSA algorithm, d is a the private key of the RSA algorithm, r is a random integer, the x₁, x₂ . . . , x_n are the secondary objects, and the x₁, x₂ . . . , x_m constitute the list of the insights.

16. A system according to claim 12, further comprising:

a payment processing module to receive a payment from the requesting party in exchange for the obfuscated selected list of the insights.

17. A system according to claim 12, further comprising:

an adjusting submodule configured to adjust a threshold for selecting the list of the insights based on a number of the insights, a specification from the receiving party, and a strength of the association between the primary object and the associated objects.

18. A system according to claim 12, further comprising:

a machine-learning module to compute the insights using a machine-learning algorithm.

19. A system according to claim 12, further comprising:

an insight-gathering module to compute the insights by the steps of;

maintaining an inventory of goods;

obtaining shopping data on the inventory of the goods; and

computing an association relationship between purchasing one good and purchasing another good based on the shopping data.

20. A system according to claim 12, wherein the association relationship is positive or negative.