crowdsourcing privacy: bay area talks

Crowdsourcing Privacy Preferences through Time and Space

Eran Toch

Bay Area Talks, June 2013

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Privacy: an Old Idea

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Privacy in the year 500 BC

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Mishnah, Baba Batra 2a-b (500 BC - 200 CE) rules:"A person should not open a window to a common yard... A person would not open to a common yard a door against door and a window against window."

The Rashbam (1080 – 1160 CE) defines the Hezek Re’iya (“the damage of being seen)

The Talmud is contextualizing the text (Bible, Numbers 24-10):“and whose eyes are opened: How beautiful are your tents, O Jacob, your dwelling places, O Israel.”

“What did Balaam see? He saw that their tents openings were not directed at each other, and said: they deserve that the divine spirit will be on them.”

Houses and Systems

Privacy challenges in the design of people's houses is not that different that challenges in online privacy of information-sharing systems.

Social Networks

Information Sharing Systems

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Multi-User Cloud Services Crowdsourcing

Apps

Enterprise Interoperability

The Architecture of Privacy

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System

User

Information Sharing Systems

System

User

Traditional Systems

What can go wrong?

Expressing Preferences

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Users find it challenging to understand the privacy landscape, to understand preferences, and to express them.

Agility and Changes

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Sharing preferences will change over time, while the content is still accessible.

Where Should Users Start?

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Agenda

‣ Crowdsourcing preferences.

‣ Predictions and defaults.

‣ Modeling longitudinal privacy.

‣ The future: design and theory.

Crowdsourcing Privacy Preferences

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Eran Toch, Crowdsourcing Privacy Management in Context-Aware Applications, Personal and Ubiquitous Computing, 2013.

Crowdsourcing and Privacy

1.Modeling user behavior by analyzing a crowd of users.

2. Identifying meaningful context and recognizing personal differences.

3. Building mechanisms that can help individuals manage preferences.

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The Question

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‣ Can we model and predict users’ preferences for a given scenario?

‣ In our case, a combination of the location reported and the target of the information.

‣ Can we build mechanisms that help users manage their access control using the predictions?

Crowdsourcing Privacy Preferences

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Aggregator

Preference

Application

Collecting preferences and their underlying context

ModelerBuilding a model for the preference

according to a context

Personalizer Personalizing the model for a specific, given user

Using the preference model in a specific application

Preference

PreferencePreference

Preference

Preference

Our User Study‣ 30 Users, 2 weeks.

‣ Smart-Spaces: Tracking locations and activities.

‣ Participants were surveyed three times a day.

‣ Asked about their willingness to share their location on a Likert scale.

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Meta-Data Survey

Place Discrimination

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21 3 4 5

Less likely to share More likely to share

Some places are considered

private

Some places are shared by almost

everybody

Distribution by Semantics

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Wilingness to Share Location (0-Low, 4-High)

density

0.0

0.1

0.2

0.3

0.4

0.5

Home

1 2 3 4

None

1 2 3 4

Public

1 2 3 4

Transit

1 2 3 4

Travel

1 2 3 4

Work

1 2 3 4

SemanticsHome

None

Public

Transit

Travel

Work

Regression Model

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‣ Predictions for a user u regarding a place k, are learned linearly.

‣ Easy to model and to compute

‣ Provides insight into variability

Prediction by place

Prediction by semantics

Personal tendency

Individual Differences‣ People can be

categorized according to their privacy approach:

‣ Privacy "Unconcerned"

‣ Privacy "Pragmatic"

‣ Privacy "Fundamentalist"

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university

everybody

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1.5 2.0 2.5 3.0 3.5 4.0

factor(fit.cluster)

1

2

3

Prediction Methods

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Prediction Method

sq

ua

red

err

or

0.0

0.2

0.4

0.6

0.8

1.0

(a)Simple (b)Semantic (c)Semanic/Biased

Predicting by place

Predicting by semantics

Semantics and personal tendency

Higher error rate

Applications of Crowdsourcing

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Applications

‣ Aggregating user models for:‣ Automatic decision making.

‣ Preference prediction for semi-manual suggestions.

‣ Defaults for new users.

‣ Providing possibilities for rule modifications.

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Decision Engine

Location

Location

Disclose | Deny | Manual

Location

Requests

Simulated Access Control

Access Control Performance

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threshold

accuracy

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

strategyA

M

SM

If we simulate an access control mechanism, we reach 80% accuracy in predicting what a user would do, and 93% accuracy when allowing the worst predictions to be examined by the user.

AutomationAccuracy

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The Price of PrivacyHow much should businesses pay for a location?

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Omer Barak, Gabriella Cohen, Alla Gazit and Eran Toch. The Price Is Right? Economic Value of Location Sharing, Submitted to MCSS: Workshop on Mobile Systems for Computational Social Science, Ubicomp 2013

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Creating Defaults

People have a tendency to stick to the defaults:

‣ Organ donation choices

‣ Access control policies

‣ Enterprise calendars (L Palen, 1999)

Johnson, Eric J. and Goldstein, Daniel G., Do Defaults Save Lives? (Nov 21, 2003). Science, Vol. 302, 2003.

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Can we create defaults that would reflect the decision space of existing users?

Selecting Defaults

Eran Toch, Norman M. Sadeh, Jason I. Hong: Generating default privacy policies for online social networks. CHI Extended Abstracts 2010: 4243-4248

Clustering Defaults

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Policy a: location within the campus

Policy b: location outside of the campus

Generalizing Defaults

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Developing clustering methods that map a large set of preferences into a manageable number of default preferences.

Ron Hirschprung, Eran Toch, and Oded Maimon. Evaluating Bi-Directional Data Agent Applicability and Design in Cloud Computing Environment, submitted to Information Systems Research

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Testing the DefaultsUsing two user studies (n=499) we have evaluated the performance of the clustering methods.

Privacy through Time

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Oshrat Rave-Ayalon and Eran Toch. Retrospective Privacy: Managing Longitudinal Privacy in Online Social Net- works, accepted to the Symposium on Usable Privacy and Security (SOUPS), 2013

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Privacy through Time‣ Digital information is

almost never erased.

‣ With search engines and timelines, it becomes more and more accessible.

‣ What are the consequences for privacy?

A Note About Forgetting

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In the past, forgetting was the default. Now, it's the other way around.

“Forgetting thus affords us a second chance, individually and as a society, to rise above our past mistakes and misdeeds, to accept that humans change over time.”

The Question‣ How information aging impacts users’ sharing

preferences in Online Social Networks?

‣ Providing a quantitative model and proof for longitudinal privacy.

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t0 tm1

Publication Time 1 month 1 year

ty1

Anticipation privacy

preferences

Retrospective privacy

The Question - cont’d‣ Guiding the design of mechanisms longitudinal

privacy:

‣ Retrospective mechanisms.

‣ Future-facing mechanisms.

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‣ A within-subject user study (n=193)

‣ Between-subject user study (n=298)

‣ Analyzing differences between users, randomly assigned to four conditions: ‣ 0-1 years‣ 1-2 years‣ 2+ years‣ Control: 0-2+ years

‣ Using a custom FB application.

Our Studies

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Willingness to Share Over Time

Willingness to share decreases with time (Spearman correlation test, ρ = -0.21, p < 0.0001)

Growth of Variability

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The variability of sharing preferences grows considerably after 1 month (Levene test for variance, F = 10.69, p < 0.00001).

Time and Self Representation

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A post is becoming less and less representative of the user over time (Kruskal-Wallis , p < 0.0001)

The Decay of Content

Irrelevancy is the major reason for considering to hide the post (61% of the cases in which users declare they wish to hide the post)

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offend

inappropriate

other

change

irrelevant

0 50 100 150 200Count

Rea

sons

for h

idin

g th

e po

st fr

om fr

iend

s

Life Changes

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Some life changes decrease the willingness to share (p < 0.05, Kruskal-Wallis test)

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Expiry Date for Information

A default expiration time of 1.5 years

Retrospective Mechanisms

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What an Engineer Would Do?

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Privacy-by-Design

‣ What should an engineer do?

‣ Classic PbD solutions such as k-anonymity, and Differential Privacy are partial.

‣ A new engineering toolbox is needed for Info-Share systems:

‣ Privacy-by-Design: Identifying Gaps and Overcoming Barriers in the Intersection of Law and Engineering,

‣ An Israel Science Foundation Project, with Prof. Michael Birnhack (TAU) and Dr. Irit Hadar (Haifa).

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The Info-Share Toolbox

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Policy-based Architecture-based

Priv

acy

Gua

rant

ee

An extension of Langheinrich's Privacy-by-Design framework

Data Minimization

ChoiceNotice and

NudgeCoaching

Recourse

‣ Ongoing project: Privacy-Peer-Pressure

‣ Empowering users to coach their friends to privacy behavior.

‣ Allowing users to copy their friends, learn from their friends, and spread good ideas around.

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Coaching

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Patrick Gage Kelley et al., Privacy as Part of the App Decision-Making Process. CHI 2013.

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Notice

Nudging

53Wang, Yang, et al. "Privacy nudges for social media: an exploratory Facebook study." WWW’13, 2013.

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Choice

Theory‣ We need effective theory to think about

privacy

‣ Softer models of privacy: privacy by obscurity (Hartzog and Stutzman, 2012).

‣ Behavioral economic approaches (Acquisti’s work).

‣ Models that analyze privacy within a social context (e.g., social capital and privacy, Ellison et al., 2011).

‣ Models that analyze the relations between code and norms.

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A New (Old) Metaphor

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Acknowledgments‣Israel Science Foundation

‣Israel Ministry of Science

‣Israel Cyber Bureau

‣Tel Aviv University

http://toch.tau.ac.il/

erant@post.tau.ac.il