Computer Science

SDAP: A Secure Hop-by-Hop Data Aggregation Protocol for Sensor

Networks

Yi Yang, Xinran Wang, Sencun Zhu and Guohong CaoApril 24, 2007

Presented by Nicky MahilaniCSC 774 In-class presentation

1•Acknowledgement: Based on slides provided by Author

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Outline

• Data Aggregation in Sensor Networks

• Security Challenges

• SDAP Details

• Performance Evaluation

• Conclusion

• Future Work

2

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Sensor Networks

• Group of sensor nodes

report to a Base Station(BS)• Without data aggregation

– Data redundancy

– Communication cost

– Energy expenditure

• Reporting raw data is

inefficient

•BS

3

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Data Aggregation in Sensor Networks

• With data aggregation

we can reduce– Data redundancy

– Communication cost

– Energy expenditure

• A lossy data compression

process

•BS

4

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Outline

• Data Aggregation in Sensor Networks

• Security Challenges

• SDAP Details

• Performance Evaluation

• Conclusion

• Future Work

5

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Security Challenges in Data Aggregation?(1)

• A compromised intermediate

node may change the

aggregated data

• BS cannot verify the result

without knowing original

readings

False Alarm

•BS

Compromised node

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• Hop-by-hop aggregation – Aggregates computed by a

higher-level node are from

‘more’ low-level nodes

– If a compromised node is

closer to BS, false value from

it has more impact on the final

result computed by BS

•Legitimate temperature (32F ~ 150F)

•BS

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Security Challenges in Data Aggregation?(2)

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Security Challenges in Data Aggregation?(3)

• Question:Can the BS obtain a good approximation of the fusion result when a fraction of nodes are compromised?

False Alarm

•BS

Compromised node

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Outline

• Data Aggregation in Sensor Networks

• Security Challenges

• SDAP Details

• Performance Evaluation

• Conclusion

• Future Work

9

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Network Model

•BS B S

. . . . . .

- An unbalanced tree rooted at BS

- Data is aggregated hop by hop

- Each aggregate is a tuple (value, count)

- Every node only forwards one copy

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Attack Model

• Goal: Inject false data without being detected by BS

• Example:

– Without modifying the

received aggregate

• (98.7F~101F, 51)

– Count change attack

• (100F~150F, *)

– Value change attack

• (32F~150F, 51)

•Legitimate temperature (32F ~ 150F)

•BS

(100F, 50)

•(?, ?)

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SDAP: Secure Hop-by-hop Data Aggregation Protocol

• Basic Principle– Divide and conquer

– Commit and attest

• Protocol Overview– Tree Construction & Query Dissemination

– Probabilistic grouping• Partition nodes into logical groups of similar size

– Hop-by-hop aggregation• Each group generates a commitment which cannot be denied later

– Verification & attestation• BS identifies suspicious groups

• Suspect groups attest correctness of commitments to BS

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Tree Construction & Query Dissemination

• Tree construction• Query dissemination

– BS * : Fagg, Sg

• Fagg:

an aggregation function,

e.g., avg, count

• Sg:

a random number as

grouping seed

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B S

. . . . . .

•Legitimate temperature (32F ~ 150F)

•avg •avg•avg •avg •avg

•avg •avg •avg •avg

•avg •avg•avg•avg•avg•avg•avg •avg

•avg•avg•avg•avg•avg•avg•avg•avg•avg

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Probabilistic grouping & data aggregation

• Probabilistic grouping is conducted through group leader selection– H(Kx, Sg|x) < Fg(c)

• x : node id

• Kx : master key of x

• H : pseudorandom function, uniform output in [0,1)

• Sg : for security and load

balance• c : count

• Fg : grouping function, [0,1)

output increasing with c

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•Legitimate temperature (32F ~ 150F)

B S

. . . . . .x

y

w '

•H(Kid, Sg|id) > Fg(1)

•H(Kw’, Sg|w’) < Fg(8)

•H(Kx, Sg|x) < Fg(15)

•H(Ky, Sg|y) < Fg(c)

Computer Science

Probabilistic grouping & data aggregation

• Probabilistic grouping is conducted through group leader selection– H(Kx, Sg|x) < Fg(c)

• x : node id

• Kx : master key of x

• H : pseudorandom function, uniform output in [0,1)

• Sg : for security and load

balance• c : count

• Fg : grouping function, [0,1)

output increasing with c

15

•Legitimate temperature (32F ~ 150F)

B S

x

D ef au lt L ead er

. . . . . .

y

w '

By choosing appropriate grouping functions, group sizes are roughly even with small deviation, providing good basis for attestation

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Group Aggregation

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• Format of aggregates

flag value count MAC id seed

•Encrypted

•Authenticated

Flag: initialized to 0, set to 1 after leaders finish group aggregation, so that other nodes on the path just forward group commitments

B S

. . . . . .

u

v

w

x

y

– uv : u, 0, E(Kuv ,1|Ru|Sg)|MACu

• MACu=MAC(Ku, 0|1|u|Ru|Sg)

• Leaf node aggregation

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Group Aggregation (2)

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B S

. . . . . .

u

v

w

x

y

– vw : v, 0, E(Kvw ,3|Aggv|Sg)|MACv

• Aggv=Fagg(Rv, Ru, Ru’)

• MACv=MAC(Kv, 0|3|v|Aggv| MACu MACu’ |Sg)

MAC is also computed hop by hop, thus representing authentication of all the nodes contributing to the data

H(Kv, Sg|v) > Fg(3)

• Immediate node aggregation

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Group Aggregation (3)

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B S

. . . . . .

u

v

w

x

y

– xBS : x, 1, E(Kx ,15|Aggx|Sg)|MACx

• Aggx=Fagg(Rx, Aggw, Aggw’)

• MACx=MAC(Kx, 1|15|x|Aggx|MACw MACw’|Sg)

H(Kx, Sg|x) < Fg(15)•Default leader of leftover nodes

•Tracking the forwarding path:• A forwarding table (incoming link, group id)• Group id is the id of group leader• Bloom filter may help scale up

• Leader node aggregation

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Verification & attestation

• BS identifies suspicious groups for attestation• Outlier detection by Grubbs’ Test

– extensions: multiple outliers, bivariate• Pc * Pvalue <α? (significance level, e.g., 0.05)

– Attackers tend to forge false values as well as large counts correspondingly, to make false values count for larger fraction in the final result

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•(x, 142F, 50) •(y, 100F, 20)•(w’, 95F, 25) •(BS, 90F, 28)

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Verification & attestation (2)

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B S

. . . . . .

u

v

w

x

y

• Forwarding attestation requests from BS

• Suppose group x is under suspicion

BS y: x, Sa, Sg

Sa: a random number as attestation seed

Node y then forwards this request to leader x

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• Group attestation− Probabilistic

attestation path selection• From x, each parent

sums up counts of all the children, then computes

• picks up ith child on the path, if

Verification & attestation (3)

21

d

kka cidSHw

1

)|(

),[1

1 1

i i

kk ccw

B S

v '

w

x

u

v

w '

u '

y

. . . . . .

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Verification & attestation (4)

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B S

v '

w

x

u

v

w '

u '

y

. . . . . .

• Attestation response from groups• Each node on the path sends back count and

reading• Sibling node sends back count, aggregate

and MAC (leaf only sends count and reading)

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Verification & attestation (5)

• Group response validation by BS

• BS reconstructs Aggx and MACx based on responses– If both match the submitted values,

accepts them– Otherwise, rejects them

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B S

v '

w

x

u

v

w '

u '

y

. . . . . .

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Outline

• Data Aggregation in Sensor Networks

• Security Challenges

• SDAP Details

• Performance Evaluation

• Conclusion

• Future Work

24

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Detection Rate

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• m is the number of attestation paths

B S

v '

w

x

u

v

w '

u '

y

. . . . . .

•Cv : Count value •m

• Det

ecti

on R

ate

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Grouping Function (Fg)

• Goal: small variations on group sizes – if c = 1, Fg(c) = 0– if c infinite, Fg(c) = 1– increase slowly in the beginning,

approach to 1 quickly after a certain value above the mean

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１ ：１ ： ０＜

)1()( Cg ecF

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Communication Overhead

• Packet*hop: 3.4k~4.4K • in a non-secure aggregation scheme: 3k • in a no aggregation secure scheme: 21k

27

12

34

56

78

910

30

35

40

45

503500

3600

3700

3800

3900

4000

4100

4200

4300

4400

Number of Attested Groups(ng): 1~10

n=3280, d=3, h=7, np=1

Group Sizes(g): 30~50

Overh

ead o

f O

ur

Pro

tocol

(packet*hop)

Computer Science

Outline

• Data Aggregation in Sensor Networks

• Security Challenges

• SDAP Details

• Performance Evaluation

• Conclusion

• Future Work

28

Computer Science

Conclusion & Future Work

• A probabilistic grouping based secure data aggregation protocol– Divide-and-conquer– Commit-and-attest – With adjustable detection rate– Low performance overhead

• Challenges:– Max/Min– Content-based attestation

• Readings from nodes in the same neighborhood should bear certain temporal/spatial correlations

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Thank you !

• Questions ???

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