anonysense: privacy-aware people-centric sensing

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AnonySense: Privacy- Aware People-Centric Sensing Cory Conelius, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin [Institute for Security Technology Studies Dartmouth College, USA] Nikos Triandopoulos [Department of Computer Science University of Aarhus, Denmark] MobiSys’08 Presented By: Leyla Kazemi

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AnonySense: Privacy-Aware People-Centric Sensing. Cory Conelius, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin [Institute for Security Technology Studies Dartmouth College, USA] Nikos Triandopoulos [Department of Computer Science University of Aarhus, Denmark] MobiSys’08 - PowerPoint PPT Presentation

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Page 1: AnonySense: Privacy-Aware People-Centric Sensing

AnonySense: Privacy-Aware People-Centric Sensing

Cory Conelius, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin

[Institute for Security Technology StudiesDartmouth College, USA]

Nikos Triandopoulos[Department of Computer Science

University of Aarhus, Denmark]

MobiSys’08

Presented By: Leyla Kazemi

Page 2: AnonySense: Privacy-Aware People-Centric Sensing

Outline

Motivation AnonySense Architecture

System Design Task Language Threat Model Trust Model

Protocol Tasking Protocol Reporting Protocol Security Properties

Evaluation

Page 3: AnonySense: Privacy-Aware People-Centric Sensing

Motivation

Personal Mobile Devices equipped with many sensors (e.g., cameras, microphones, accelerometers)

Opportunity for cooperative sensing applications

Users Contributing data to information services

Challenge: Protecting the user privacy while their devices reliably contribute data

Page 4: AnonySense: Privacy-Aware People-Centric Sensing

Motivation Opportunistic sensing: Leveraging

users’ mobile devices to collectively measure environmental data (context)

Introducing people-centric, dynamic, and highly mobile communication

Applications: CarTel, Mobiscopes, Urbanet, Senseweb, Metrosense

Examples: Finding Parking Spots, Locating lost Bluetooth-enabled objects, collecting traffic reports of a street

Sensor-enabled cellphones

Page 5: AnonySense: Privacy-Aware People-Centric Sensing

Challenges

Dependent on a large-scale, and heterogeneous personal devices

Should be implemented across autonomous wireless access points, and public internet

Protecting users’ privacy

Page 6: AnonySense: Privacy-Aware People-Centric Sensing

Privacy Issue

Report includes time and location of the sensor revealing user’s location at that time

Integrity of system and reliability of report User trusted??

Wilshire, Stanley Ave, 9003610:00am

Mar20th 2009

Page 7: AnonySense: Privacy-Aware People-Centric Sensing

AnonySense A privacy-aware architecture for

realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices

Allowing applications to submit sensing tasks that will be distributed across anonymous devices

Receiving verified, yet anonymized sensor data reports

App

System

Task Anonymous Verified Report

Page 8: AnonySense: Privacy-Aware People-Centric Sensing

System Components

Mobile Nodes (MN): Sensing Computation Memory Wireless communication

Carrier: carries the mobile node

Page 9: AnonySense: Privacy-Aware People-Centric Sensing

Components

Registration Authority (RA): Registering nodes

Verifying the proper installation on the MNs Verifying the attributes of the MNs Installing a private group key on the node

Issuing certificates to task service and report service Apps and nodes can later verify the authenticity of the

services

Page 10: AnonySense: Privacy-Aware People-Centric Sensing

Components

Task Service (TS): Receiving Task descriptions from apps Performing Consistency checking Distributing current task to MNs Returning a token to app for later retrieving the

tasked data Report Service (RS):

Receiving reports from MNs Aggregating them for more privacy Responding to queries from apps

Page 11: AnonySense: Privacy-Aware People-Centric Sensing

Components

Mix Network (MIX): Anonymizing channel between MNs and RS De-linking reports submitted by MNs Allowing users to anonymously send messages How:

waiting for enough incoming messages before sending messages to the next node

Delaying and mixing of messages makes it difficult to correlate incoming and outgoing messages

Page 12: AnonySense: Privacy-Aware People-Centric Sensing

Task Language AnonyTL : A language

for applications to specify their tasks

Acceptance Conditions Report Statements Termination Conditions

(Task 25043) ( Expires 1196728453)(Accept (= @carrier ‘ professor ’ ) )

( Report ( location SSIDs ) ( Every 1 Minute )( In location

( Polygon ( Point 1 1) ( Point 2 2)( Point 3 0 ) ) ) )

(Task 25044) ( Expires 1210392000)(Accept (< temperature 0 ) )

( Report ( location time temperature )( Every 5 Minute )

( and (< temperature 0) (< humidity 2 0 ) ) )( Report ( location time temperature humidity )

( Every 10 Minute )( and (> temperature 20) (> humidity 8 0 ) ) )

Page 13: AnonySense: Privacy-Aware People-Centric Sensing

Threat Model Carrier Anonymity

De-anonymizing a carrier by linking a report to the carrier Eavesdropping on communication between MN and APs Submitting tasks, and retrieve the reports Registering as MN

Data Integrity Tampering with the sensor data Submitting bogus reports to RS Impersonating the RS to deliver bogus reports to the apps Tampering with MN hardware or software

Other threats (Not considered) Tampering directly with MN sensors Denial-of-service threats

Page 14: AnonySense: Privacy-Aware People-Centric Sensing

Trust Model

Carrier Trusting the node software to properly implement

AnonySense Protocol Mobile Nodes

Communicating with TS, and RS using WiFi APs MN trusting the RA to certify the identities of TS and RS RA certifying each MN as valid using a group signature MN trusting RA to certify authenticity of each task

Applications Trusting RA to certify TS and RS Trusting TS to deploy tasks as requested Trusting MN to correctly execute tasks Apps are not authenticated

Page 15: AnonySense: Privacy-Aware People-Centric Sensing

Trust Model

Registration Authority Trust Nothing

TS/RS Trusting RA to certify valid MNs only Not trusting apps

Certifying MNs Running proper version of AnonyTL Verifying the MN’s attributes Providing MN with a group signature MN maintain

anonymous

Page 16: AnonySense: Privacy-Aware People-Centric Sensing

Protocol

Tasking Protocol : Getting tasks from apps to mobile nodes Task Generation Task Verification Response to App Tasking Nodes

Reporting Protocol: MNs reporting sensor data back to apps Data fusion Data retrieval MAC address recycling

Security Properties

Page 17: AnonySense: Privacy-Aware People-Centric Sensing

Tasking Protocol Task Generation

App generates the task, sends it to TS using SSL ensuring true TS receives it

Specifies an expiration date in the task TS generates a unique ID for the task

Task Verification If syntax is valid, TS sends it to RA RA computes k, if k >kg, RA prepares certificate RA sends the certificate (hash of the task, and task ID) to TS

Response to App If task is incorrect, or k< kg, TS sends a message to App Otherwise, TS replies to App with a task ID with a TS-signed certificate

Tasking nodes Polls the TS for tasks MN uses anonymous authentication to prove its validity using its group

signature TS delivers all tasks to MN Some nodes will repeatedly retrieve the same tasks

Page 18: AnonySense: Privacy-Aware People-Centric Sensing

Reporting Protocol

MN signs each report using a group-signature Encrypts it with the RS public key MIX network delivers reports to RS in a “mixed” fashion

Data fusion RS aggregates reports from a task Reports combined using k-anonimity

Data Retrieval App polls the RS for available data using enc. Channel App presents the TS-signed token to prove its authority

MAC address recycling MN might be tracked using static MAC address MN changes its MAC everytime so that report and task actions may not

be linked

Page 19: AnonySense: Privacy-Aware People-Centric Sensing

AnonySense Architecture

Page 20: AnonySense: Privacy-Aware People-Centric Sensing

Security Properties Adversary can learn little by eavesdropping on MN

communication all communications are encrypted

Adversary cannot pose as TS/RS MNs and Apps have certificate from RA for public key of TS/RS

TS cannot link MN’s tasks each arrives from one MAC address/ intervals are randomized

Adversary can learn little to pose as App any task must satisfy k> kg

Adversary cannot link MN’s reports each arrives from one MAC address/ intervals are randomized / uses MIX

Page 21: AnonySense: Privacy-Aware People-Centric Sensing

Evaluation

Implementation Communication

SSL-encrypted HTTP channel MN encrypts its report with MIX node keys, sending

messages using SMTP Servers

Written in Ruby PL Mobile Nodes

Nokia N800 Software in C++ Downloading tasks using libcurl Verifying using RSA/ SHA-1 No MAC address rotation

Page 22: AnonySense: Privacy-Aware People-Centric Sensing

Evaluation

Applications RogueFinder

Detecting rogue APs in a given area Tasking AnonySense to report all APs visible to MNs Sensor: MN’s Wi-Fi interface

ObjectFinder Finding the bluetooth Mac address of a lost object After detecting the specified MAC address, MN reports

the current location

Page 23: AnonySense: Privacy-Aware People-Centric Sensing

Experimental Results

Overall Result MN detected 84 unique APs, of which RogueFinder found

12 as rogues

Average time for MN receiving task from RF, later reporting it : 15.5 sec

Average power cost: 6. 64 mW

Complete task-scan-report cycle cost : 0.11J 17 times smaller than MP3-quality audio streaming

Page 24: AnonySense: Privacy-Aware People-Centric Sensing

Experimental Results

Data Transfer

Page 25: AnonySense: Privacy-Aware People-Centric Sensing

Experimental Results

Overall Energy Consumption

Page 26: AnonySense: Privacy-Aware People-Centric Sensing

Experimental Results

Detailed Energy Consumption

Page 27: AnonySense: Privacy-Aware People-Centric Sensing

Discussion Scalability

Reduce Increasing burden on MN TS could give MN only a subset of tasks MN rejecting some tasks when overloaded

Carrier Policy Configuring a policy on which tasks to accept

Attribute-based tasking Using other techniques to further enforce anonymity like “statistical k-

anonymity” Task Dissemination

App may receive much more reports than needed AnonySense allow removing a task

Delay Tolerance The more carriers, the less latency in message passing using MIX

Data Quality More accurate data Less privacy for users Allowing applications to request a certain granularity of either time or

location

Page 28: AnonySense: Privacy-Aware People-Centric Sensing

Summary

AnonySense: A comprehensive system to preserve privacy of users in opportunistic-sensing environments

Allowing applications to request sensor data using task language

Data collected in opportunistic, delay-tolerant manner

Data reported, while the users are anonymized but verified

Page 29: AnonySense: Privacy-Aware People-Centric Sensing

Thanks