Compromising Location Privacy in Wireless Networks Using Sensors with Limited Information

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


The Problem

• How to find out the positions of fixed wireless nodes?


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?


Naïve Solution 2

• Directive sender

If I don't’ have enough money to buy directive

sensors?


Problem

• How to compromising location privacy

in wireless networks using sensors with Limited Information ?


Solution

• Step 1. Deploy sensors (spies) among wireless nodes to eavesdrop data– We know the position of deployed sensors

Nodes Nodes + Sensors


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


Solution

• Step 2. Use Principal Component Analysis (PCA) to estimate node numbers in this area


Principal Component Analysis (PCA)

• An important statistics technique

511 grade

531 gradeMike

IQ

511 grade

531 grade

The first (principal) component

The second component


Principal Component Analysis (PCA)

• This skill can be applied to 3 or more dimensional data


What can we do with PCA?

• Suppose we draw a point for a time period...

Packet # of Sensor 1

[x,x,13,x,x,…]



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)


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 !!


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


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.


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


What can we do with BSS?

• Trick: We cut the whole area into many overlapped blocks

1




2




3




4




This square belongs to 4 blocks



• For each block, we apply BSS to get many separated signals

[How are you]




[How are you][How or you]

{Cab sin}





{Cab sin}

[How are youth]





{Cab sin}

[How are youth] [haha you]



• Cluster the separated signals together based on similarity


{Cab sin}

[How are youth] [haha you]

noise, ignore

Cluster 1



• By analyzing the overlap of signals, we can estimate the position of them.

[How are you]


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


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


Q & A

Compromising Location Privacy in Wireless Networks Using Sensors with Limited Information

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