c3p: context-aware crowdsourced cloud privacy (at pets 2014)
TRANSCRIPT
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C3P: Context-Aware Crowdsourced Cloud Privacy
1CloudSpacesPrivacy Enhancing Technologies Symposium, 2014
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2
Files to Flowers
Conversion
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2
Files to Flowers
Conversion
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2
Files to Flowers
Conversion
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2
Files to Flowers
Conversion
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2
Files to Flowers
Conversion
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3
60% increase in corporate data shared to the cloud in 2015
Source: Elastica’s Q2 2015 Shadow Data Report
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3
20% of files shared to the cloud contain protected data
60% increase in corporate data shared to the cloud in 2015
Source: Elastica’s Q2 2015 Shadow Data Report
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3
20% of files shared to the cloud contain protected data
60% of sensitive files contain PII
30% …contain health info
60% increase in corporate data shared to the cloud in 2015
Source: Elastica’s Q2 2015 Shadow Data Report
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3
20% of files shared to the cloud contain protected data
60% of sensitive files contain PII
30% …contain health info
Emergence of “Shadow IT”
60% increase in corporate data shared to the cloud in 2015
Source: Elastica’s Q2 2015 Shadow Data Report
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You cannot use cloud services.
You are fully protected.
Your files are always
encrypted before
uploading.
Anti-Snooping Tools for the Cloud
Examples:
4
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You cannot run
software.
You are fully protected.
Your files are always
quarantined.
What if Antivirus Software was Similar?
5
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Obstacles
Privacy vs. Services dilemma
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Obstacles
Privacy vs. Services dilemma
Context-dependence of privacy
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Obstacles
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
Privacy vs. Services dilemma
Context-dependence of privacy
Manual effort and expertise for assessing data sensitivity
6
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What is needed?
Ensure serviceable protection instead of brute encryption.
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What is needed?
Ensure serviceable protection instead of brute encryption.
Account for the metadata, sharing environment, and
data content.
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What is needed?
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
Ensure serviceable protection instead of brute encryption.
Account for the metadata, sharing environment, and
data content.
Automatically estimate the sensitivity of shared data.
7
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Introducing C3P
Various levels of information hiding
8
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Introducing C3P
Define data in terms of context
Various levels of information hiding
8
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Introducing C3P
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
Private crowdsourcing mechanism for gathering people privacy policies
Define data in terms of context
Various levels of information hiding
8
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Introducing C3P
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
Private crowdsourcing mechanism for gathering people privacy policies
Psychologically grounded approach for estimating sensitivity
Define data in terms of context
Various levels of information hiding
8
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Fine-Grained Policies
9
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Defining Data through Context
10
Content Metadata Environment
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Defining Data through Context
10
Content Metadata Environment
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Defining Data through Context
10
Content Metadata Environment
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Location
Data
TopicMedia
Home
OfficeDocument
SoftwareFinancial
Educational
Context Vocabulary
11
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Privacy Preserving Crowdsourcing
12
Business Me ColleagueFinancial Me Stranger Faces Home Friend
Financial Me Stranger
Business Me Colleague
Faces Home Friend
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
User 1 User 2 User 3
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Privacy Preserving Crowdsourcing
12
Business Me ColleagueFinancial Me Stranger Faces Home Friend
Financial Me Stranger
Business Me Colleague
Faces Home Friend
Faces Home Friend
Sharing Operation Context
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
User 1 User 2 User 3
![Page 30: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/30.jpg)
Privacy Preserving Crowdsourcing
12
Business Me ColleagueFinancial Me Stranger Faces Home Friend
Financial Me Stranger
Business Me Colleague
Faces Home Friend
Faces Home Friend
WorkSea
Colleague
Family
Sharing Operation Context
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
User 1 User 2 User 3
![Page 31: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/31.jpg)
Privacy Preserving Crowdsourcing
12
Business Me ColleagueFinancial Me Stranger Faces Home Friend
Financial Me Stranger
Business Me Colleague
Faces Home Friend
Faces Home Friend
WorkSea
Colleague
Family
Forward-AnonymityK-anonymity
Sharing Operation Context
I dedicate the rest of my life for sorting out
sensitive from non-sensitive files on my HD
User 1 User 2 User 3
Faces Home Friend
Context
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Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
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Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
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Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
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High Privacy Attitude
75%
Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
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High Privacy Attitude
75%
Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
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High Privacy Attitude
75%
Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
![Page 38: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/38.jpg)
High Privacy Attitude
75%
Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
Group Invariance
Faces Home Friend Faces Home Friend
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High Privacy Attitude
75%
Sensitivity Estimation using Item Response Theory
13
Faces Home Friend
High Sensitivity 75%
Group Invariance
Faces Home Friend Faces Home Friend
Item Invariance
![Page 40: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/40.jpg)
Connecting the Dots
14Client
Server
?
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Connecting the Dots
14Client
Server
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Context Extraction
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Context Extraction
Sensitivity Request
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Sensitivity Reply
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Sensitivity Reply
Policy Decision
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Policy Decision
Data Sharing
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Crowdsourcing
?
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Connecting the Dots
14
Financial Me Stranger
Client
Server
Crowdsourcing
? Sensitivity Computation
![Page 49: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/49.jpg)
Evaluation
15
C3P
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IRT Models Fit Privacy-Aware Cloud Sharing?
16
81
96
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IRT Models Fit Privacy-Aware Cloud Sharing?
• Ex: With which privacy level would you share a project presentation with a friend?
16
81
96
![Page 52: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/52.jpg)
IRT Models Fit Privacy-Aware Cloud Sharing?
• Ex: With which privacy level would you share a project presentation with a friend?
• Standardized Infit Statistic:• (x-axis values should lie in [-2,2])
16
81
96
Dichotomous case
Sens
itivi
ty
Infit t-statistic
A dot represents a
context
![Page 53: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/53.jpg)
IRT Models Fit Privacy-Aware Cloud Sharing?
• Ex: With which privacy level would you share a project presentation with a friend?
• Standardized Infit Statistic:• (x-axis values should lie in [-2,2])
16
81
96
Dichotomous case
Sens
itivi
ty
Infit t-statistic
A dot represents a
context
![Page 54: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/54.jpg)
IRT Models Fit Privacy-Aware Cloud Sharing?
• Ex: With which privacy level would you share a project presentation with a friend?
• Standardized Infit Statistic:• (x-axis values should lie in [-2,2])
16
81
96
Polytomous case
Infit t-statisticSe
nsiti
vity
Dichotomous case
Sens
itivi
ty
Infit t-statistic
A dot represents a
context
![Page 55: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/55.jpg)
IRT Models Fit Privacy-Aware Cloud Sharing?
• Ex: With which privacy level would you share a project presentation with a friend?
• Standardized Infit Statistic:• (x-axis values should lie in [-2,2])
16
81
96
Polytomous case
Infit t-statisticSe
nsiti
vity
Dichotomous case
Sens
itivi
ty
Infit t-statistic
A dot represents a
context
![Page 56: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/56.jpg)
IRT Models Fit Privacy-Aware Cloud Sharing?
• Ex: With which privacy level would you share a project presentation with a friend?
• Standardized Infit Statistic:• (x-axis values should lie in [-2,2])
16
81
96
Yes!
Polytomous case
Infit t-statisticSe
nsiti
vity
Dichotomous case
Sens
itivi
ty
Infit t-statistic
A dot represents a
context
![Page 57: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/57.jpg)
Temporal Cost of Crowdsourcing & Privacy
17
Zipf context distribution
500
3125
30000
av.: 1 Item/6 hours
• Synthetic Dataset:
![Page 58: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/58.jpg)
Temporal Cost of Crowdsourcing & Privacy
k
17
Zipf context distribution
500
3125
30000
av.: 1 Item/6 hours
• Synthetic Dataset:
![Page 59: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/59.jpg)
Temporal Cost of Crowdsourcing & Privacy
k
17
Zipf context distribution
500
3125
30000
av.: 1 Item/6 hours
• Synthetic Dataset:
Crowdsourcing cost: Hit rate (HR) from 0 to 90% in 10 days
![Page 60: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/60.jpg)
Temporal Cost of Crowdsourcing & Privacy
k
17
Zipf context distribution
500
3125
30000
av.: 1 Item/6 hours
• Synthetic Dataset:
Crowdsourcing cost: Hit rate (HR) from 0 to 90% in 10 daysAnonymity cost: HR difference starts high but vanishes in 10 days.
![Page 61: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/61.jpg)
Effect of Sharing Behavior on the Temporal Cost
18
Anonymity Parameter K=3
500
3125
30000
av.: 1 Item/6 hours
• Synthetic Dataset:
![Page 62: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/62.jpg)
Effect of Sharing Behavior on the Temporal Cost
18Effect: Long tail distribution is of lower temporal cost.
Anonymity Parameter K=3
500
3125
30000
av.: 1 Item/6 hours
• Synthetic Dataset:
![Page 63: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/63.jpg)
Robustness Towards Malicious Users?
19
• Test: • Assign sensitivities to items
and attitudes to people. • Honest users choose policies
according to the model. • Malicious users choose
policies at random.
![Page 64: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/64.jpg)
Robustness Towards Malicious Users?
19
• Test: • Assign sensitivities to items
and attitudes to people. • Honest users choose policies
according to the model. • Malicious users choose
policies at random.
![Page 65: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/65.jpg)
Robustness Towards Malicious Users?
19
• Test: • Assign sensitivities to items
and attitudes to people. • Honest users choose policies
according to the model. • Malicious users choose
policies at random.
![Page 66: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/66.jpg)
Robustness Towards Malicious Users?
19
• Test: • Assign sensitivities to items
and attitudes to people. • Honest users choose policies
according to the model. • Malicious users choose
policies at random.
Preset Sensitivity
Computed Sensitivity-Check
![Page 67: C3P: Context-Aware Crowdsourced Cloud Privacy (at PETS 2014)](https://reader035.vdocuments.mx/reader035/viewer/2022062900/58eb4b091a28abfc3f8b45e1/html5/thumbnails/67.jpg)
Robustness Towards Malicious Users?
19
• Test: • Assign sensitivities to items
and attitudes to people. • Honest users choose policies
according to the model. • Malicious users choose
policies at random.
Preset Sensitivity
Computed Sensitivity-Check
Tolerance: 25% malicious: ≈8% difference, 50% malicious: ≈17% difference
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Future Work
• Recommendation of policies to users
• Batch sensitivity analysis
• Considering probabilistic attacks on the scheme
• Working with IRT alternatives.
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ELO MF
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PrivyShare
PrivyShare - Desktop
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PrivyShare Benefits
• Works with any cloud service
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PrivyShare Benefits
• Works with any cloud service• Provides “Sensitivity as a Service”
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PrivyShare Benefits
• Works with any cloud service• Provides “Sensitivity as a Service”• Offers fine grained protection• Metadata cleaning• Face Blurring• Encryption• Encryption + Auxiliary Information (automatic summaries, blurred
thumbnails)
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PrivyShare
PrivyShare - Browser
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PrivySeal: Dealing with 3rd Party Cloud Apps
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PrivySeal
privyseal.epfl.ch
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Images/Media Credits
•Pixel77•Freepik