Privacy workgroup

Privacy workgroup Privacy workgroup - Start

2016-06-01 54K 54 0 0

Privacy workgroup - Description

Participants. Ashwin. . Machanavajjhala. (leader). Suman. Nath (scribe). Kristen . Lefevre. Evimaria. . Terzi. Alan . Mislove. Ranga. . Raju. . Vatsavai. Jennifer Neville. Hakan. . Hacigumus. Mohamed Mokbel . ID: 344134 Download Presentation

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Privacy workgroup




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Presentations text content in Privacy workgroup

Slide1

Privacy workgroup

Slide2

Participants

Ashwin

Machanavajjhala

(leader)

Suman

Nath (scribe)

Kristen

Lefevre

Evimaria

Terzi

Alan

Mislove

Ranga

Raju

Vatsavai

Jennifer Neville

Hakan

Hacigumus

Mohamed Mokbel

Slide3

Various Facets

Data security

Data privacy: Secret but useful

Data compliance

User facing: how to specify privacy, do they understand? Avoid surprise

Trust:

Slide4

Privacy

Desummarization

of data, reverse of clustering/aggregation

Examples:

Social

:

facebook

releases statistics,

fb

makes friends suggestions, personalized recommendation/ads based on friends' likes

Mobile: publish mobility traces, or aggregates

 

Issues:

Information propagation through links: S, through correlation of contexts: M

Information granularity small : S

# entities accessing data is large : CSM

Sparser data, higher dimensions: unique for individuals : SM

Multiple owners of data : CS

Different access control policies for different people, different context: SM

Unstructured data: text/speech/pictures: makes access control harder: SM

Location privacy: M

Slide5

Data compliance

Many

formal verification

problems: not our area

We can help implementing

efficiently in

system, auditing

, ensuring policies are implemented right

 

Issues: C

Complexity of auditing diverse systems

Flow of information through multiple parties: compliance (

Zynga

using data through

fb

)

Forget : what if index/models have been built from data

Corporations can by each other

Apps contain third party libraries accessing private data

Do we need mandatory access control

Slide6

User Facing

How to get informed

consent?

Issues:

Users are content manager: SM

Number of decisions is large: share to whom, what context, at what granularity (goal: reduce number of decisions, make the process more intuitive): SM

Unreadable TOS: C (PL?)

Misinterpreting apps as the platform: C (HCI?)

Users don’t understand ease of access of data: CS (HCI?)

Accountability/understandability in model (recommendations/

etc

): SM (Mining?)

What can you learn about me? As a friend, as a random person? (by crowdsourcing?) S (ML

?)

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