A Framework for Secure Data Aggregation in Sensor Networks

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

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

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

5. Security challenges in aggregation? (1) BS • 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 Yi Yang - SDAP

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

7. BS 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 Yi Yang - SDAP

8. Legitimate temperature (32F ~ 150F) BS (?, ?) (100F, 50) 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) The combination of count and value change attacks, and collusion among compromised nodes are more destructive! Yi Yang - SDAP

9. Observations Legitimate temperature (32F ~ 150F) • 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 BS Yi Yang - SDAP

10. 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 Yi Yang - SDAP

11. 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 Tree Construction & Query Dissemination • Tree construction • Similar to TAG • Query dissemination • BS   * : Fagg, Sg • Fagg: an aggregation function, e.g., avg, count • Sg: a random number as grouping seed Legitimate temperature (32F ~ 150F) Yi Yang - SDAP

12. Probabilistic grouping & data aggregation • Probabilistic grouping is conducted through group leader selection • H(Kx, Sg|x) < Fg(c) Legitimate temperature (32F ~ 150F) • 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 H(Ky, Sg|y) < Fg(c) H(Kx, Sg|x) < Fg(15) H(Kw’, Sg|w’) < Fg(8) H(Kid, Sg|id) > Fg(1) Yi Yang - SDAP

13. Probabilistic grouping & data aggregation • Probabilistic grouping is conducted through group leader selection • H(Kx, Sg|x) < Fg(c) Legitimate temperature (32F ~ 150F) • 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 By choosing appropriate grouping functions, group sizes are roughly even with small deviation, providing good basis for attestation Yi Yang - SDAP

14. Authenticated id flag count value seed MAC Encrypted Group aggregation (1) • Format of aggregates Flag: initialized to 0, set to 1 after leaders finish group aggregation, so that other nodes on the path just forward group commitments • Leaf node aggregation • uv : u, 0, E(Kuv ,1|Ru|Sg)|MACu MACu=MAC(Ku, 0|1|u|Ru|Sg) H(Ku, Sg|u) > Fg(1) Yi Yang - SDAP

15. Group aggregation (2) • Immediate node aggregation • vw : 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) Yi Yang - SDAP

16. Group aggregation (3) • Leader node aggregation • xBS : x, 1, E(Kx ,15|Aggx|Sg)|MACx Aggx=Fagg(Rx, Aggw, Aggw’) MACx=MAC(Kx, 1|15|x|Aggx|MACw MACw’|Sg) Default leader of leftover nodes H(Kx, Sg|x) < Fg(15) • 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 Yi Yang - SDAP

17. Verification & attestation(1) • BS identifies suspicious groups for attestation • Outlier detection by Grubbs’ Test • Hypothesis test: H0 vs. H1 • Our extensions: multiple outliers, bivariate • Pc * Pvalue <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 (w’, 95F, 25) (x, 142F, 50) (y, 100F, 20) (BS, 90F, 28) Yi Yang - SDAP

18. Verification & attestation(1) • BS identifies suspicious groups for attestation • Outlier detection by Grubbs’ Test • Hypothesis test: H0 vs. H1 • Our extensions: multiple outliers, bivariate • Pc * Pvalue <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 (w’, 95F, 25) (x, 142F, 50) (y, 100F, 20) (BS, 90F, 28) Yi Yang - SDAP

19. Verification & attestation(2) • Forwarding attestation requests from BS • Suppose group x is under suspicion • BS  y: x, Sa, Sg • Node y then forwards this request to leader x • Sa: a random number • as attestation seed Yi Yang - SDAP

20. Verification & attestation(3) • 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 A node with larger count has more chances to be attested Yi Yang - SDAP

21. Verification & attestation(4) • 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) Yi Yang - SDAP

22. 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 Yi Yang - SDAP

23. 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 Yi Yang - SDAP

24. Thank you! • Questions? Yi Yang - SDAP