Method and apparatus for utility-aware privacy preserving mapping in view of collusion and composition

A privacy-preserving, data-mapping technique applied in the field of utility-aware privacy-preserving mappings and devices in view of collaboration and composition

Inactive Publication Date: 2016-05-25
THOMSON LICENSING SA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In either of the above two cases, a privacy risk arises because some of the collected data may be considered sensitive by the user (e.g., political opinions, health status,

Method used

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  • Method and apparatus for utility-aware privacy preserving mapping in view of collusion and composition
  • Method and apparatus for utility-aware privacy preserving mapping in view of collusion and composition
  • Method and apparatus for utility-aware privacy preserving mapping in view of collusion and composition

Examples

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example 1

[0069] - Example 1: Synergy when considering a single private data and multiple public data;

[0070] - Example 2: Collaboration when multiple private data and multiple public data are considered;

[0071] - Example 3: Composition when considering single private data and multiple public data;

example 4

[0072] - Example 4: Combination when multiple private data and multiple public data are considered.

[0073] In example 1, private data S with two public data X 1 and x 2 Associated. In this example, we consider political opinions as private data S and TV ratings as public data X 1 , and regard snack evaluation as public data X 2 . Two privacy-preserving maps are applied on these public data respectively to obtain two published data Y that are provided to the two entities 1 and Y 2 . For example, distorted TV evaluation (Y 1 ) is provided to Netflix, and distorted snack ratings (Y 2 ) were supplied to Kraft Foods. Design privacy-preserving mappings in a decentralized manner. Each privacy-preserving mapping scheme is designed to protect S away from the corresponding analyst. If Netflix and Kraft (Y 2 ) exchange information (Y 1 ), then the user's private data (S) are compared to if they depended solely on Y 1 or Y 2 may be recovered more precisely. When analysts...

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Abstract

The present embodiments focus on the privacy-utility tradeoff encountered by a user who wishes to release some public data to an analyst, which is correlated with his private data, in the hope of getting some utility. When multiple data are released to one or more analyst, we design privacy preserving mappings in a decentralized fashion. In particular, each privacy preserving mapping is designed to protect against the inference of private data from each of the released data separately. Decentralization simplifies the design, by breaking one large joint optimization problem with many variables into several smaller optimizations with fewer variables.

Description

[0001] Cross References to Related Applications [0002] This application claims the following U.S. Provisional Application Serial No. 61 / 867,544, filed August 19, 2013, entitled "Method and Apparatus for Utility-Aware Privacy Preserving Mapping in View of Collusion and Composition" This provisional application is hereby incorporated by reference in its entirety for all purposes. [0003] This application is related to US Provisional Patent Application Serial No. 61 / 691,090, filed August 20, 2012, entitled "A Framework for Privacy against Statistical Inference," (hereinafter "Fawaz"). This provisional application is expressly incorporated herein by reference in its entirety. [0004] Additionally, this application is related to (1) Attorney Docket No. PU130120, entitled "Method and Apparatus for Utility-Aware Privacy Preserving Mapping against Inference Attacks," and (2) Attorney Docket No. PU130122 , entitled "Method and Apparatus for Utility-Aware Privacy Preserving Mapping...

Claims

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Application Information

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IPC IPC(8): G06F21/62
CPCG06F21/6245
Inventor 纳蒂亚·法瓦兹阿巴萨利·马克杜米·卡克哈基
Owner THOMSON LICENSING SA
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