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Overview
1. Introduction
2. Urban Sensing Examples3. Applications Examples4. Security
Challenges
a. Confidentiality and Privacy Issuesb. Integrity Issuesc.
Availability Issuesd. Challenges in Participatory Sensing
5. Conclusion
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Introduction
Opportunistic people centric sensingo Small devices carried by
people that sense informationo Direct or indirect relation to human
activityo Environmental conditions
Advantages
o Leverage millions of deviceso No need to manually deploy
o Highly mobile and accessible Disadvantages
o High risks in security
o Data integrity
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Urban Sensing ExamplesCarTel
Maps traffic patterns
BikeNet
Bicycle network infrastructure
CenceMe
User activity social networkingCarTel Interface
BikeNet InterfaceCenceMe Interface
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Application Examples
Urban data collection and processing
o Large scale online data collectiono Being able to locate lost
objectso Measuring the flow of bicycles in an urban center
Environmental monitoring at the human level
o Optimize energy usage for heating and coolingo Personal
Environmental Impact Report
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Security Challenges Overview
Challenges
1. Context privacy2. Anonymous tasking3. Anonymous data
reporting
4. Reliable data readings
5. Data authenticity6. System integrity
7. Preventing data suppression8. Participation
9. Fairness
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Confidentiality and Privacy IssuesContext Privacy
Problems
It is cumbersome for users to specify fine grain policies Once
the data is on the server who can access the h/w
Solutions
Virtual wallso Group settings in categories
o Only information outside the wall can be seen Faces
o Data changes according to who is viewing
Future Research
o Determining what data can be used without being able toinfer
other data
o Grabbing only enough data for application purpose
without sacrificing usability
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Confidentiality and Privacy IssuesAnonymous Tasking
Problems
By tasking specific users it is possible to gain
personalinformation
Determining reliability of participants could reduce
anonymitySolutions
Tasking Serviceo Users download all tasks and selectively choose
which to
do
Attribute based authentication
o Users reveal only their attributes
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Confidentiality and Privacy IssuesMasking Users' Location
Blind Tasking
Transfer data to other nodes before uploadingo Overall routing
structure must be protectedo Data needs to be encrypted to not be
intercepted
Hitchhiking
o Only include characteristics about locationo Disadvantageous
for limited popularity
Introduce blur and random jittero Decreases accuracy
oAmount of error needs to be constrained
Automatic Spatiotemporal Blurringo Generalize location through
large geographical tileso Only upload data when enough sets are
available
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Integrity IssuesReliable Data Storage
Problems
Any participant with an appropriately configured device
canreport falsified data
Devices are controlled by users
Incentives to mask private informationSolutions
Redundancyo Task cloningo Fixed sensor ground truth
Game Theory
o Reputation based system
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Integrity IssuesData Authenticity
Problems
Tampered data during transit Current schemes correspond to fixed
sensors where there is
a stable topological tree that spans sensors
Solutions
Cryptographoically enhanced error-correcting techniqueso
Encrypted data that shows if it has been tampered with
Group signaturesoAllows multiple groups to use a single
verifying signature
o Cracked signatures and be redistributed without takingdown the
entire infrastructure
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Integrity IssuesSystem Integrity
Problems
Tasks need to have their source verified Data received needs to
be accurate and temporally relevant
Solutions
Task specific languages
Secure crytographic stateso Provide topological, temporal and
user-
related parameters to validate the information received.
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Availability IssuesPreventing Data Suppression
Denial of Service (DoS) due to devices ignoring taskrequests
Network availability of devices Data consuming applications
could be killed by users
If users are unable to control the data access, they are
lesslikely to carry the device or permit tasks to be performed
Distributed DoS (DDoS) Attack
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Availability IssuesParticipation
Problems
Users must have incentives to gain mass participation Difficult
to convince giving away private information with little
to no benefit
Solutions
Convenience is key to appeal Provide incentives that are
compatible with users' needs and
interests Privacy-aware hybrid payoff model
o Beneficial services vs privacy loss they experience
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Challenges in Participatory Sensing
Users are tasked and have to manually partake in
gatheringinformation
Additional security challenges arise as the user may leakmore
information than the task specifies
o Taking a picture of a menu on a table
Integrity becomes difficult as the user can fabricate sensordata
or not provide the correct results of the task
o Ratings of a restaurant
4 Rivers Smokehouse Google User Review
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Conclusion
Opportunistic people centric sensing
Most applications contain personal information Securing that
information becomes key
o Providing a service that people would want to participate
o Keepings users data secure as to not be harmed
o Even obscuring the data may not be enough for
completeanonymity
Participatory sensing needs additional security thought
Questions?
