Monitoring and managing user privacy levels

• US 8,893,287 B2
• Filed: 03/12/2012
• Issued: 11/18/2014
• Est. Priority Date: 03/12/2012
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Gjonej, Gerard, Li, Ying
• Primary Examiner(s): Cervetti, David Garcia
• Application Number:
  US13/417,363
• Publication Number:
  US 20130239220A1
• Time in Patent Office: 981 Days
• Field of Search: 726/26
• US Class Current: 726/26
• 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: G06F 21/6263 during internet communication, e.g. revealing personal data from cookies
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1433 Vulnerability analysis
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Various embodiments pertain to techniques for measuring a user'"'"'s privacy level as a user interacts with various web services. In various embodiments, entities with which the user interacts are detected and sensitive information shared by the user is logged to determine what a given entity knows about the user. In some embodiments, sensitive information that is shared by a user can be processed using a predictive algorithm to ascertain a user'"'"'s level of privacy. When a user'"'"'s identity is predicted by the algorithm, a user can be alerted to the loss of anonymity. In various embodiments, user-defined areas of anonymity can be used to measure a user'"'"'s definition of privacy. In some embodiments, alerts can also be provided to the user when a new, previously undisclosed, piece of information is shared by the user.

Claims

1. A computer-implemented method comprising:

receiving data previously disclosed by a user to one or more entities;

determining, by a privacy monitoring module at one or more computing devices, whether an identity of the user disclosing the data can be determined by the one or more entities from the previously disclosed data, the determining comprising entering at least a portion of the previously disclosed data into a predictive algorithm, and determining that the identity of the user can be determined if the identity of the user is predicted by the predictive algorithm; and

responsive to determining that the identity of the user can be determined by the one or more entities from the previously disclosed data, transmitting a loss of privacy notice to a device associated with the user.

2. The computer-implemented method of claim 1, wherein receiving the data previously disclosed by the user further comprises receiving one or more of:

data transmitted as part of a user query;

data input into a web page;

data extracted from HTML, script, or other code of a web page;

data regarding a called web page;

data regarding an IP address from which a request for a web page was transmitted;

user login credential information;

information regarding a user'"'"'s previous request for a web page;

or

data regarding time spent viewing a web page.

3. The computer-implemented method of claim 1, further comprising:

grouping the previously disclosed data into one or more groups based on the one or more entities to which the data was disclosed.

4. The computer-implemented method of claim 3, wherein grouping the previously disclosed data into one or more groups further comprises grouping the previously disclosed data into one or more groups based on a web page to which the data was disclosed.

5. The computer-implemented method of claim 3, wherein grouping the previously disclosed data into one or more groups further comprises grouping the previously disclosed data into one or more groups based on a group of connections on a social network to which the data was disclosed.

6. The computer-implemented method of claim 3, wherein determining whether the identity of the user disclosing the data can be determined by the one or more entities from the previously disclosed data further comprises:

entering at least a portion of the previously disclosed data in one of the one or more groups into the predictive algorithm for finding data in a data index; and

determining, based on the data index, if the identity of the user can be determined from the previously disclosed data.

7. The computer-implemented method of claim 6, wherein the determining that the identity of the user disclosing the data can be determined is performed responsive to:

a name of the user being returned as a top result in the data index, a name of the user being returned on a first page of results in the data index, identifying information unique to the user being returned as a top result in the data index, or identifying information unique to the user being returned on a first page of results in the data index.

8. The computer-implemented method of claim 6, wherein the determining the identity of the user disclosing the data can be determined is performed responsive to determining, based on the previously disclosed data, a likelihood of a match to the user.

9. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a device, cause the device to perform operations comprising:

receiving a plurality of data points previously disclosed by a user to an entity;

selecting a combination of the plurality of data points previously disclosed by the user to the entity;

determining, based on comparing the combination of the plurality of data points previously disclosed by the user to the entity with a data index, whether sensitive information corresponding to the user can be obtained by the entity based on the combination of the plurality of data points; and

outputting a notification to a device associated with the user to indicate that the sensitive information is obtainable by the entity based on the combination.

10. The one or more computer-readable storage media of claim 9, wherein the sensitive information comprises one or more of:

information regarding the user'"'"'s educational accomplishments, information regarding the user'"'"'s current job, information regarding the user'"'"'s page on a social network, the user'"'"'s name, the user'"'"'s date of birth, a location of the user'"'"'s residence, the user'"'"'s phone number, the user'"'"'s email address, the user'"'"'s credit card number, the user'"'"'s medical history, or the user'"'"'s health insurance information.

11. The one or more computer-readable storage media of claim 9, further comprising outputting at least one of:

a listing of various pieces of sensitive information obtainable by the entity, or a periodic report showing sensitive information obtainable by the entity over a history of the user'"'"'s interaction with the entity.

12. The one or more computer-readable storage media of claim 9, wherein the plurality of data points previously disclosed by the user to the entity comprise one or more of:

data transmitted as part of a user query;

data input into a web page;

data extracted from HTML, script, or other code of a web page;

data regarding a called web page;

data regarding an IP address from which a request for a web page was transmitted;

user login credential information;

information regarding a user'"'"'s previous request for a web page;

or

data regarding time spent viewing a web page.

13. A device comprising:

one or more processors;

one or more computer-readable storage media; and

one or more modules embodied on the one or more computer-readable storage media and executable under the influence of the one or more processors, the one or more modules configured to;

detect data intended for disclosure by a user of the device to an entity;

transmit the data intended for disclosure to a privacy monitoring service prior to the data being disclosed to the entity; and

receive, from the privacy monitoring service, a loss of anonymity notice indicating that an identity of the user of the device can be determined by the entity if the data intended for disclosure is disclosed to the entity.

14. The device of claim 13, wherein the entity comprises one of:

a web page;

a plurality of web pages;

or

a group of connections on a social network.

15. The device of claim 13, wherein the data intended for disclosure comprises one or more of:

data to be transmitted as part of a user query;

data input into a web page;

data extracted from HTML, script, or other code of a web page;

data regarding a called web page;

data regarding an IP address from which a request for a web page was transmitted;

user login credential information;

information regarding a user'"'"'s previous request for a web page;

or

data regarding time spent viewing a web page.

16. The device of claim 13, wherein the one or more modules are further configured to:

determine that the data intended for disclosure has not previously been disclosed to the entity; and

provide a notice that the data intended for disclosure has not been previously disclosed to the entity.

17. The device of claim 16, wherein the data intended for disclosure is determined to not have been previously disclosed to the entity based on a comparison of the data intended for disclosure with data previously disclosed to the entity.

18. The device of claim 13, wherein the loss of anonymity notice requests that the user verify disclosure of the data intended for disclosure to the entity.

19. The device of claim 13, wherein the loss of anonymity notice includes an option for the user to determine what the entity can learn about the user if the data intended for disclosure is disclosed to the entity.

20. The device of claim 13, wherein the privacy monitoring service is configured to determine that the identity of the user can be determined by the entity based on the data intended for disclosure and data previously disclosed to the entity.