Towards Next Generation Secure DDDAS/Infosymbiotics Systems

Transcription

1 ICCS 2015, Reykjavik, Iceland June 2015 Towards Next Generation Secure DDDAS/Infosymbiotics Systems Li Xiong and Vaidy Sunderam Students: Layla Pournajaf, Daniel Garcia-Ulloa, Xiaofeng Xu Dept. of Math and Computer Science Emory University AFOSR DDDAS FA

2 DDDAS as a Unifying Paradigm Ability to dynamically integrate generated data into an application; feedback loop to steer measurement Acquisition measurements, streams, databases Assimilation preprocessing, aggregation, fusion Analytics simulations, decisions, knowledge discovery Action incorporate new results, feedback to above Platforms & Domains Internet of Things (IoT), Smart(er) Systems Physical, chemical, biological, engineering, weather Medical, health, transport, infrastructure, military, disaster Trends: InfoSymbiotics Big data and Big computing Evolution: ubiquitous sensing/informatics/multimodal

3 From the Sensor-Scale to the Exa-Scale Hierarchical DDDAS Devices Embedded devices Sensors UAV/UGV Participants Regional/Central HPC Clusters Exascale machines Data/knowledge bases Networking

4 Multilevel DDDAS Systems End-to-end data/compute/control flow & interaction *Original figure due to Dr. Frederica Darema

5 Next Generation DDDAS/InfoSymbiotics Systems Participant/data privacy Identity, location and data are all sensitive Uncertainty Measurements/observations subject to error At exascale, intermittent failures are inevitable Cloaking/obfuscation for privacy Handle privacy & uncertainty within unified rubric Aggregation, fusion and summarization Transformations in the presence of uncertainty Secure high-performance multiparty computation At each DDDAS level, perform local computations and analytics, cooperatively with mutually untrusted peers

6 Foundational Work Privacy Preserving Data Collection with Feedback Control Privacy Preserving Data Aggregation with Feedback Control Secure Data Collection and Aggregation Privacy Preserving Feedback Control Cloaking Aggregation Prediction Collection Perturbation Correction Privacy Preserving Data Collection Sensitive Data Streams Privacy Preserving Data Aggregation Aggregated Data streams Data Modeling Data Contributors Trusted Aggregator Application

7 Next Generation DDDAS Privacy-preserving, secure acquisition } High-performance Fusion/aggregation of uncertain data secure distr. comp. Prediction/correction/application steering + feedback loop

8 Privacy Preserving Participant Management Feedback-controlled assignment of cloaked mobile participants to targets Task management feedback Measurement feedback Input/steering data Challenges: maximize coverage, minimize cost; handle mobile participants/targets

9 DDDAS Feedback-driven Tasking a) Exact Trajectories b) Uncertain Trajectories Predictive/Corrective scheme augmented with mobility model Model: Meas: Pred: Update: Xt p(xt Xt 1) Zt p(zt Xt) Z1:t = Z1,..., Zt p(xt Z1:t 1) = Σ p(xt Xt 1) p(xt 1 Z1:t 1) p(xt Z1:t) = p(yt Xt) p(xt Z1:t 1) Σ p(yt Xt) p(xt Z1:t 1)

10 Data Assimilation under Uncertainty Objective: Aggregation/fusion of unreliable observations for analytics/decision-making Spatio-temporal crowdsensing example: M participants (unreliably) report about N events at one or more of R consecutive times Observations S = {s 1, s 2, s v } or (missing) Determine state label at location l j at time t k

11 Truth Inference Approach Hidden Markov Model using iterative approach to determine transition probabilities Algorithm summary Initial guess history + heuristics Seek max posterior probability Semi- and un-supervised learning Challenges: methods for other aggregation/ fusion/assimilation functions with uncertain data

12 High-performance Distributed SMC Secure Multi-Party Computation Guarantees that computation does not reveal private input Possible approaches Shamir s secret sharing scheme Perturbation based Homomorphic encryption schemes Efficiency (secure sum) 12

13 DDDAS Software Toolkit Scalable and stateless distributed computing Small footprint for sensors and field devices Low latency, low power communications Adopt models/features from FreshBreeze/ROS/HELib Deployable at field regional levels, interfaces to traditional supercomputer simulations Algorithm libraries for SMC, distributed computation Building block modules (multiplication, division, matrix inversion) Higher level functions (distributed Kalman filter, statistical summarization, global optimization functions) Challenge: robust uncertainty-resilient implementations adaptively balancing utility (accuracy) and efficiency 13

14 Summary Next generation DDDAS/Infosymbiotics systems Ever expanding platforms Internet of Things, Smart Systems Unified systems/software model for numerous applications Requirements and expectations Privacy and security of participants, data, computation Uncertainty resilience to errors, faults, obfuscation, (mis)trust Autonomous local and hierarchical analytics, decision makeing The PREDICT project Feedback driven dynamic management of sensor-participant systems with privacy protection Trust-aware data synthesis, aggregation and validation Secure high-performance distributed computing software

15 Thank you Acknowledgements AFOSR DDDAS FA Project team Investigators: Li Xiong, Vaidy Sunderam Students: Liyue Fan, Slawek Goryczka, Layla Pournjaf, Daniel Garcia-Ulloa, Xiaofeng Xu Project URL AFOSR DDDAS FA