Instance-privacy Preserving Crowdsourcing (HCOMP2014)

Instance-privacy Preserving Crowdsourcing

Hiroshi Kajino1, Yukino Baba2, Hisashi Kashima3"

1: The University of Tokyo, JSPS Research Fellow DC2"2: National Institute of Informatics, " JST ERATO, Kawarabayashi Large Graph Project 3: Kyoto University

Nov. 4, 2014 1 HCOMP-2014

15 min

Overview

■  Instance Privacy"□  Sensitive information in a task instance leaks"

■  Instance Clipping Protocol"□  Exploit the locality of (task, privacy)"

■  Proposed Evaluation Method"□  Define the task result quality & the amount of privacy invasion"

■  Experiment"□  Evaluate the performance on a task to detect faces in images"

Nov. 4, 2014 HCOMP-2014 2

A privacy issue in submitting tasks in crowdsourcing

Overview





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Example of Privacy Invasion

■  Case Study: Task to hide faces in an image"

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Sensitive information in a task instance leaks"

Instance Result Worker Instruction"

Process the instance following the instruction

Example of Privacy Invasion

■  Case Study: Task to hide faces in an image"

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Task instance can be used for other purposes"

Instance Result Worker Instruction"

Sensitive information"of Mr. A

Linking between PIs (ex. face)"& his properties!

(place where he is, friendship, action)

Mr. A

Research Questions

■  Question 1: Design of a Privacy-Preserving protocol"□  General guideline to design a PP protocol"

■  Question 2: Performance measures for PP protocols"□  Quality of a result & privacy leakage:"

•  PP protocol must involve some operation on instance"•  Quality can be degraded by the operation"

■  Assumptions"□  Instance: Multi-way array (image, text, audio, video)"□  Result & sensitive info: Label"

Nov. 4, 2014 HCOMP-2014 6

Our concerns are a PP protocol and a performance metric for it

Overview





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Case Study: Instance Clipping Protocol

■  Instance Clipping Protocol"□  Moving C × C pixels window by C/2 pixels □  Clip the instance to generate sub-instances"□  Submit a task with the sub-instances"

Nov. 4, 2014 HCOMP-2014 8

Illustrate a guideline by the IC protocol

C

C/2 Clipping window

sub-instances"

Key Insight: Locality of Task

■  Locality Property of Task"□  Local task: Local area has sufficient info for task execution"

ex) Face detection, name & license number transcription"

□  Global task: Global area is necessary for task execution"ex) abstract of a meeting (audio/text)" place, friendship, action detection (image)"

Nov. 4, 2014 HCOMP-2014 9

Task execution is classified by its dependency on instance

Apple tries to buy a fingerprint security company


Key Insight: Locality of Privacy

■  Locality Property of Privacy"□  Local privacy: Local area has sufficient info for privacy invasion"

ex) Face, name, license number"

□  Global privacy: Global area is necessary for privacy invasion"ex) abstract of a meeting (audio/text)" place, friendship, action (image)"

Nov. 4, 2014 HCOMP-2014 10

Privacy invasion is classified by its dependency on instance



Basic Idea: Instance Transformation

■  Local-info-preserving transformation"□  Preserve local info, mash global info (e.g., Clipping)"□  Suitable for (local task, global privacy)"

Nov. 4, 2014 HCOMP-2014 11

Privacy preservation using opposite properties of task & privacy

Basic Idea: Instance Transformation

■  Local-info-preserving transformation"□  Preserve local info, mash global info (e.g., Clipping)"□  Suitable for (local task, global privacy)"

■  Global-info-preserving transformation"□  Preserve global info, mash local info (e.g., Blurring)"□  Suitable for (global task, local privacy)"

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Privacy preservation using opposite properties of task & privacy

Overview





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Evaluation: Motivation

■  Proposed Evaluation Method"□  Two metrics:!

•  Quality of task result"•  Privacy invasion"

□  Requirements:!•  Privacy: Should penalize even if wrong info leaks"

–  Suffer from a wrong rumor"– Checking answers with ground truths is not appropriate"

•  Task: Have the same unit with the privacy measure"Nov. 4, 2014 HCOMP-2014 14

Trade-off between task result quality and privacy leakage

Task result quality ☺"Privacy invasion "

Task result quality #"Privacy invasion ☺

Trade-off

Evaluation: Proposed Metrics

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Task execution process Privacy invasion process

Model

Worker samples task result of instance In from p(R | In) 　　　　　　　　　　R: Result or sensitive info"　　　　　　　　　　I: Instance"　　　　　　　　　　p(R | I): Instruction"

Measures

EI[ KL(p(R | I) || p’(R | I)) ]

■  Difference from the original"■  Smaller, better

(Mutual info between R & I )"　　　= EI[ KL(p’(R | I) || p’(R)) ]

■  Info leakage of R from I ■  Even wrong info should not leak from I ■  Smaller, better

w/o PP w/ PP

Design measures based on a worker model

0

1

face/non-face

Evaluation: Modeling

□  Instance: I □  Result: R □  Worker = Sampling from p(R | I)

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Worker is modeled as a sampler

Result Worker

instance instruction"

0

1

行動1/行動2 =

Prob dist"

Face/non-face

Random var

Evaluation: Proposed Metrics

Nov. 4, 2014 HCOMP-2014 17

Task execution process Privacy invasion process

Model

Worker samples task result of instance In from p(R | In) 　　　　　　　　　　R: Result or sensitive info"　　　　　　　　　　I: Instance"　　　　　　　　　　p(R | I): Instruction"

Measures

EI[ KL(p(R | I) || p’(R | I)) ]

■  Info loss caused by PP"■  Smaller, better

(Mutual info between R & I )"　　　= EI[ KL(p’(R | I) || p’(R)) ]

■  Info leakage of R from I ■  Even wrong info should not leak from I ■  Smaller, better

w/o PP w/ PP

Design measures based on a worker model

0

1

face/non-face

Overview





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Experiment: Setting

■  Stanford 40 Actions Dataset [Yao+, 11]"□  Task: Detect faces in images"□  Privacy: Infer the action s/he takes given a sub-instance"

•  10 choices:!cooking, fishing, running, playing Frisbee, watching TV,"feeding horse, playing guitar, texting, using computer,"writing on note"

□  Normalize images to fit in 500 × 500 pixels

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Investigate properties of the IC protocol using the proposed metrics

Experiment: Result

■  Performance on Different Hyperparameters"□  Task quality: C = 100 is 1.1 times worse than C = 300 □  Privacy leakage: C = 300 is 1.9 times worse than C = 100 ⇒ Able to preserve privacy w/o degrading quality!

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Proposed metrics can capture the trade-off

50 100 150 200 250 300C: clipping window size [pixels]

0.14

0.16

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Task

info

rmat

ion

loss

[bit

s]

1.0

1.5

2.0

2.5

3.0

Priv

acy

info

rmat

ion

loss

[bit

s] Privacy"leakage

Task quality

Better

Size of sub-instance C [pixel]

Conclusion

■  Our Contributions"□  Design a PP Protocol: !

•  Exploit different properties of (task, privacy)"

□  Evaluation Method:!1)  Modeling task execution & privacy invasion processes"2)  Propose metrics using the model"

□  Experiment:!•  Evaluated the performance of the IC protocol"•  Proposed evaluation method could capture the trade-off"

Nov. 4, 2014 HCOMP-2014 21

We propose a design principle of PP protocols as well as an evaluation method

Reference [Yao+, 11] Yao, B., Jiang, X., Khosla, A., Lin, A. L., Guibas, L., & Fei-Fei, L. (2011). Human action recognition by learning bases of action attributes and parts. In Proceedings of 2011 IEEE International Conference on Computer Vision (ICCV) (pp. 1331–1338)."

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Instance-privacy Preserving Crowdsourcing (HCOMP2014)

Data & Analytics

Transcript of Instance-privacy Preserving Crowdsourcing (HCOMP2014)