Post on 15-Dec-2015
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Compromising Location Privacy in Wireless Networks
Using Sensors with Limited Information
Author: Ye Zhu and Riccardo Bettati
Department of computer science, Texas A&M University
Presenter: Kai-shin Lu
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
The Problem
• How to find out the positions of fixed wireless nodes?
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Naïve Solution 1
• He tells me (eavesdrop)I am in
Atanosoff.
I want to order one pizza.
If I can protect my position information?
(e.g. cloaking, encrypting)
If I don’t need any location service?
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Naïve Solution 2
• Directive sender
If I don't’ have enough money to buy directive
sensors?
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Problem
• How to compromising location privacy
in wireless networks using sensors with Limited Information ?
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Solution
• Step 1. Deploy sensors (spies) among wireless nodes to eavesdrop data– We know the position of deployed sensors
Nodes Nodes + Sensors
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Solution
• Step 1. (Continue)– The sensors only collect the time series of
packet counts– E.g. [100,200,13] I got 100 packets during
0-10 seconds.
I got 200 packets during 11-20 seconds.
I got 13 packets during the next 10 seconds.
Control center
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Solution
• Step 2. Use Principal Component Analysis (PCA) to estimate node numbers in this area
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Principal Component Analysis (PCA)
• An important statistics technique
511 grade
531 gradeMike
IQ
511 grade
531 grade
The first (principal) component
The second component
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Principal Component Analysis (PCA)
• This skill can be applied to 3 or more dimensional data
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with PCA?
• Suppose we draw a point for a time period...
Packet # of Sensor 1
[x,x,13,x,x,…]
Packet # of Sensor 2
Packet # of Sensor 3
138
6
[x,x,8,x,x,…]
[x,x,6,x,x,…]
The red point represents the 3rd time period’s data.
Its coordinate is(13,8,6)
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with PCA?
• Draw all points– Is there any hidden factor behind these data?
There are 2 wireless nodes in
this area !!
Yes! There are 2 hidden factors which greatly affect the data !!
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Solution
• Step 2. Use Principal Component Analysis (PCA) to estimate node numbers in this area
• Step 3. Then use Blind Source Separation (BSS) to estimate the positions of nodes
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Blind Source Separation (BSS)
• BSS was originally developed to solve the cocktail party problem– Which can extract one person’s voice signal
given a mixtures of voices at a cocktail party
Hi Mike, how are you doing today?
…So I went to HyVee yesterday.
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Nice property of BSS
• Get unmixed singles from mixed signals
• Suppose sensor 1 got : [5, 0, 1, 0, 1 ]– Apply BSS, we can get unmixed signals
• One is [3,0,0,0,0] – which might come from Node A• One is [2,0,0,0,1] – which might come from Node B• One is [0,0,1,0,0] – which might be noise
Sensor 1Node A Node B
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• Trick: We cut the whole area into many overlapped blocks
1
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• Trick: We cut the whole area into many overlapped blocks
2
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• Trick: We cut the whole area into many overlapped blocks
3
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• Trick: We cut the whole area into many overlapped blocks
4
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• Trick: We cut the whole area into many overlapped blocks
This square belongs to 4 blocks
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• For each block, we apply BSS to get many separated signals
[How are you]
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• For each block, we apply BSS to get many separated signals
[How are you][How or you]
{Cab sin}
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• For each block, we apply BSS to get many separated signals
[How are you][How or you]
{Cab sin}
[How are youth]
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• For each block, we apply BSS to get many separated signals
[How are you][How or you]
{Cab sin}
[How are youth] [haha you]
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• Cluster the separated signals together based on similarity
[How are you][How or you]
{Cab sin}
[How are youth] [haha you]
noise, ignore
Cluster 1
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
What can we do with BSS?
• By analyzing the overlap of signals, we can estimate the position of them.
[How are you]
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Solution summary
• By PCA, we know that there are n nodes
• Cut whole area into many overlapped blocks
• Apply BSS in each block– Get many separated (unmixed) signals
• Cluster them together based on similarity
• Pick up n largest clusters
• Use overlap analysis to estimate the positions of nodes
Iowa State University Department of Computer ScienceSoftware Engineering Laboratory
Discuss
• Good:– If the nodes are fixed, then it provides a cheap
way to get their positions even though the data are perfectly encrypted
• Bad:– The nodes should be fixed– If nodes can manipulate signal power, the
overlap analysis part will fail– It assume that the communications among
sensors won’t affect normal data collecting