Computer Science
CSC 774 Advanced Network Security
Enhancing Source-Location Privacy in Sensor
Network Routing (ICDCS ’05)
Brian Rogers
Nov. 21, 2005
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Introduction and Motivation
• Major challenge to deployment of sensor
networks is privacy
• Two types of privacy
– Content-oriented privacy (e.g. packet data)
– Contextual privacy (e.g. source location of packet)
• Important use of future sensor network
applications is asset monitoring
– Source-location privacy is critical
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Example Scenario
source
sink
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Outline
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Panda-Hunter Game
Formal & Simulation Models
Baseline Routing
Routing with Fake Sources
Phantom Routing
Privacy for Mobile Sources
Conclusions & Future Work
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Panda-Hunter Game
• Once panda is detected, source periodically
sends data to sink through multi-hop routing
• Assume single panda, source, and sink
• Attacker:
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Non-malicious
Device-Rich
Resource-Rich
Informed
• Privacy cautious routing technique prevents
hunter from locating source
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Formal Model
• Asset monitoring network: sixtuple (N, S, A, R, H, M)
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N = set of sensor nodes
S = network sink
A = asset being monitored
R = routing policy of sensors to protect asset
H = hunter with movement rules M to capture asset
• Two privacy metrics for a routing strategy R
– Φ = safety period of an R given M
– L = capture likelihood of R given M
• Network performance
– Energy Consumption (# messages sent)
– Delivery Quality (avg. msg. latency, delivery ratio)
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Simulation Model
• N = 10,000 nodes
• Panda appears at random location, and closest
sensor periodically sends packets to the sink
• Simulation ends if hunter gets close to panda
(i.e. within Δ hops) or hunter fails to catch
panda within a threshold time
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Baseline Routing Techniques
• Two most popular routing techniques for
sensor networks
– Flood-based Routing
• Source node forwards packets to all neighbors
• When a neighbor receives a packet, if it has not already
seen this packet, it forwards the packet to all its
neighbors with probability Pforward
– Single-path (Shortest-path) Routing
• Initial configuration phase sets up lists at sensor nodes
so each node knows which neighbor is on the shortest
path to the sink
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Patient Adversary Model
• Hunter starts at sink
• When hunter hears a message, it moves to the
message’s immediate sender
• Process repeats until hunter reaches source
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Baseline Routing Performance
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Baseline Routing Performance (2)
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Routing with Fake Sources
• Flooding and single-path routing have poor
source-privacy:
– Add fake sources to inject fake packets
– Lead hunter away from real source
• Two Issues
– How to choose the fake source?
– How often to inject fake packets?
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Routing with Fake Sources (2)
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Routing with Fake Sources (3)
• Fake sources still not enough
• Smarter Adversary can detect zigzag pattern
• Pick one of the two directions and follow to
the source
• If this is not the real source, backtrack to reach
the other source
• Fake messaging increases energy cost for little
increase in source-location privacy
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Phantom Routing
• Problem with baseline and fake messaging
techniques:
– Sources provide a fixed route so adversary can
trace each route
• Goal of phantom routing:
– Direct hunter away from source to phantom source
• Two Phases
– Random walk: direct msg. to phantom source
– Flooding/single-path routing: direct msg. to sink
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Phantom Routing (2)
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Phantom Routing (3)
• Random Walk Phase
– Source-location privacy depends on phantom source being
far from real source after hwalk hops
• True Random Walk
– Not good: Message tends to hover around real source
– Proof in paper using central limit theorem
• Directed Random Walk
– Sector-based: Each node knows east/west
– Hop-based: Each node knows toward/away from source
– Pick one direction randomly and each node during random
walk sends the msg. to another node in that direction
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Phantom Routing (4)
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Phantom Routing (5)
• New adversary: Cautious Adversary Model
– Since hunter may be stranded far from true source
and not hear any messages for some time
– If no message heard for some time interval,
backtrack one step and wait again
• Results worse for cautious adversary, so it is
better for hunter to be patient and wait for
messages to arrive
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Privacy for Mobile Sources
• How does source location privacy change if asset is
mobile (e.g. panda walks around)
• Tests using a simple movement pattern:
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α:
δ:
d:
T:
governs direction
stay time at each location
distance of each movement
reporting interval
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Privacy for Mobile Sources
• Impact of panda’s velocity
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Privacy for Mobile Sources
• Impact of hunter’s hearing range
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Conclusions & Future Work
• Conclusions
– Flooding and single-path routing have poor source location
privacy
– Phantom routing can be used with either routing protocol to
greatly enhance privacy at a small cost of communication
overhead
• Future Work
– Authors: Investigate stronger adversarial models and
multiple asset tracking scenarios
– Multiple hunters: Could they collude to find panda faster
– Multiple sinks: Sensors transmit to randomly chosen sink
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