1. A Framework for Secure Data Aggregation in Sensor Networks Yi Yang Xinran Wang, Sencun Zhu and Guohong Cao The Pennsylvania State University MobiHoc’ 06

2. SDAP2 Why data aggregation? (1) Without data aggregation –Data redundancy –Communication cost –Energy expenditure BS Many low-cost sensors Some data sinks which subscribe to special data streams by distributing interests or querying

3. SDAP3 Why data aggregation? (2) With data aggregation Reduce data redundancy, communication cost and energy expenditure in data collection! BS

4. SDAP4 Network model An unbalanced tree rooted at BS Data are aggregated hop by hop Each aggregate is a tuple (value, count) Every node only forwards one copy BS

5. SDAP5 Security challenges in aggregation? (1) A compromised node may report a false fusion result, causing the final aggregation result to be much different from the true measurement. Question: –How can BS obtain a good approximation of the fusion result when a fraction of nodes are compromised? Compromised node False Alarm BS

6. SDAP6 Attack model Example: –Without modifying the received aggregate (98.7F~101F, 51) –Count change attack (100F~150F, *) –Value change attack (32F~150F, 51) Goal: Inject false data without being detected by BS Legitimate temperature (32F ~ 150F) BS (100F, 50) (?, ?) The combination of count and value change attacks, and collusion among compromised nodes are more destructive!

7. SDAP7 Our solutions Divide and conquer Commit and attest Tree construction and query dissemination Probabilistic grouping –Partition nodes in the tree into multiple logical groups (subtrees) of similar size Hop-by-hop aggregation –Each group generates a commitment which cannot be denied later Attestation between BS and suspicious groups –BS identifies abnormal groups from the set of received group commitments –Groups under suspicion prove the correctness of submitted commitments to BS BS discards commitments from groups failing to support previous values when computing final aggregates

8. SDAP8 Tree Construction & Query Dissemination Tree construction –Similar to TAG Query dissemination –BS   * : F agg, S g F agg : an aggregation function, e.g., avg, count S g : a random number as grouping seed Legitimate temperature (32F ~ 150F) avg

9. SDAP9 Probabilistic grouping & data aggregation Probabilistic grouping is conducted through group leader selection –H(K x, S g |x) < F g (c) x : node id K x : master key of x H : pseudorandom function, uniformly maps the input into the range of[0,1) S g : for security and load balance c : count value F g : grouping function, outputs a real number between [0,1) output increasing with c Legitimate temperature (32F ~ 150F) H(K id, S g |id) > F g (1) H(K w’, S g |w’) < F g (8) H(K x, S g |x) < F g (15) H(K y, S g |y) < F g (c)

10. SDAP10 Probabilistic grouping & data aggregation Probabilistic grouping is conducted through group leader selection –H(K x, S g |x) < F g (c) x : node id K x : master key of x H : pseudorandom function, uniform output in [0,1) S g : for security and load balance c : count F g : grouping function, [0,1) output increasing with c By choosing appropriate grouping functions, group sizes are roughly even with small deviation, providing good basis for attestation Legitimate temperature (32F ~ 150F)

11. SDAP11 Group aggregation (1) Format of aggregates flagvaluecountMACidseed Encrypted Authenticated Leaf node aggregation –u  v : u, 0, E(K uv,1|R u |S g )|MAC u MAC u =MAC(K u, 0|1|u|R u |S g ) Flag: initialized to 0, set to 1 after leaders finish group aggregation, so that other nodes on the path just forward group commitments H( K u, S g |u) > F g (1)

12. SDAP12 Immediate node aggregation –v  w : v, 0, E(K vw,3|Agg v |S g )|MAC v Agg v =F agg (R v, R u, R u’ ) MAC v =MAC(K v, 0|3|v|Agg v | MAC u MAC u’ |S g ) Group aggregation (2) MAC is also computed hop by hop, thus representing authentication of all the nodes contributing to the data H( K v, S g |v) > F g (3)

13. SDAP13 Leader node aggregation –x  BS : x, 1, E(K x,15|Agg x |S g )|MAC x Agg x =F agg (R x, Agg w, Agg w’ ) MAC x =MAC(K x, 1|15|x|Agg x |MAC w MAC w’ |S g ) Group aggregation (3) H( K x, S g |x) < F g (15) Default leader of leftover nodes

14. SDAP14 Verification & attestation(1) Outlier detection by Grubbs’ Test an existing work BS needs to verify the correctness of the aggregated value

15. SDAP15 Verification & attestation(2) Forwarding attestation requests from BS Suppose group x is under suspicion –BS  y: x, S a, S g –Node y then forwards this request to leader x S a : a random number as attestation seed

16. SDAP16 Probabilistic attestation path selection –From x, each parent sums up counts of all the children, then computes. Finally determine the path by picking up ith child on the path, if Verification & attestation(3) Group attestation A node with larger count has more chances to be attested

17. SDAP17 Each node on the path sends back count and reading Sibling node sends back count, aggregate and MAC (leaf only sends count and reading) Verification & attestation(4) Attestation response from groups

18. SDAP18 Verification & attestation(5) Group response validation by BS BS reconstructs Agg x and MAC x based on responses –If both match the submitted values, accepts them –Otherwise, rejects them

19. SDAP19 Security Analysis An attacker can not selectively compromise nodes to ensure his optimal attacking A compromised node can not know in advance whether 1.it will become a group leader or which group it will belong to 2.its aggregate will become an outlier by Grubbs’ test 3.it will be selected on the attestation path

20. SDAP20 Detection Rate m is the number of attestation paths

21. SDAP21 Communication Overhead Packet*hop: 3.4k~4.4K in a non-secure aggregation scheme: 3k in a no aggregation secure scheme: 21k

22. SDAP22 Thank you! Questions? if a node has a larger count value, the probability for it to become a leader is higher. So if a compromised node with large count be- comes a leader, the BS will definitely reject it and the whole large group, which will also affect the quality of aggregation.