1. A Framework for Secure Data Aggregation in Sensor Networks Yi Yang Joint work with Xinran Wang, Sencun Zhu and Guohong Cao Dept. of Computer Science & Engineering The Pennsylvania State University

2. Yi Yang - SDAP2 Sensor networks Functions –Sensing –In-network processing –Ad-hoc communication Applications –Real-time traffic monitor –Military surveillance –Homeland security Berkeley Mica Motes BS

3. Yi Yang - SDAP3 Why data aggregation? (1) Without data aggregation –Data redundancy –Communication cost –Energy expenditure BS Reporting raw data is unnecessary!

4. Yi Yang - SDAP4 Why data aggregation? (2) With data aggregation Reduce data redundancy, communication cost and energy expenditure in data collection! BS

5. Yi Yang - SDAP5 Security challenges in aggregation? (1) A lossy data compression process –Individual sensor readings are lost in aggregation A compromised intermediate node may change the aggregated data BS cannot verify the result without knowing original readings Compromised node False Alarm BS

6. Yi Yang - SDAP6 Security challenges in aggregation? (2) Question: –How can BS obtain a good approximation of the fusion result when a fraction of nodes are compromised? Compromised node False Alarm BS ?

7. Yi Yang - SDAP7 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

8. Yi Yang - SDAP8 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!

9. Yi Yang - SDAP9 Observations 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

10. Yi Yang - SDAP10 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

11. Yi Yang - SDAP11 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

12. Yi Yang - SDAP12 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 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)

13. Yi Yang - SDAP13 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)

14. Yi Yang - SDAP14 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)

15. Yi Yang - SDAP15 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)

16. Yi Yang - SDAP16 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 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

17. Yi Yang - SDAP17 Verification & attestation(1) Outlier detection by Grubbs’ Test –Hypothesis test: H 0 vs. H 1 –Our extensions: multiple outliers, bivariate P c * P value <α? (significance level, e.g., 0.05) One-sided test for count and two-sided test for values –Attackers tend to forge false values as well as large counts correspondingly, to make false values count for larger fraction in the final result BS identifies suspicious groups for attestation (x, 142F, 50)(y, 100F, 20)(w’, 95F, 25)(BS, 90F, 28)

18. Yi Yang - SDAP18 Verification & attestation(1) Outlier detection by Grubbs’ Test –Hypothesis test: H 0 vs. H 1 –Our extensions: multiple outliers, bivariate P c * P value <α? (significance level, e.g., 0.05) One-sided test for count and two-sided test for values –Attackers tend to forge false values as well as large counts correspondingly, to make false values count for larger fraction in the final result BS identifies suspicious groups for attestation (x, 142F, 50)(y, 100F, 20)(w’, 95F, 25)(BS, 90F, 28)

19. Yi Yang - SDAP19 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

20. Yi Yang - SDAP20 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) Group attestation A node with larger count has more chances to be attested

21. Yi Yang - SDAP21 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

22. Yi Yang - SDAP22 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

23. Yi Yang - SDAP23 Conclusion & future work Analysis and simulation results are skipped A probabilistic grouping based secure data aggregation protocol –Divide-and-conquer –Commit-and-attest Challenges: –Max/Min –Content-based attestation Readings from nodes in the same neighborhood should bear certain temporal/spatial correlations

24. Yi Yang - SDAP24 Thank you! Questions?