The PREDICT Project: Enhancing DDDAS/Infosymbiotics ... · INFORMS 2015, Philadelphia, PA, 3...
Transcript of The PREDICT Project: Enhancing DDDAS/Infosymbiotics ... · INFORMS 2015, Philadelphia, PA, 3...
The PREDICT Project: Enhancing DDDAS/Infosymbiotics Systems with
Privacy and Security
Li Xiong and Vaidy SunderamStudents: Layla Pournajaf, Daniel Garcia-Ulloa, Xiaofeng Xu
Dept. of Math and Computer ScienceEmory University
INFORMS 2015, Philadelphia, PA, 3 November 2015
AFOSR DDDAS FA9550-12-1-0240
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
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
Multilevel DDDAS Systems
• End-to-end data/compute/control flow & interaction
*Original figure due to Dr. Frederica Darema
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
Foundational Work
• Privacy Preserving Data Collection with Feedback Control
• Privacy Preserving Data Aggregation with Feedback Control
• Secure Data Collection and Aggregation
Privacy Preserving Data Collection
Privacy Preserving Data Aggregation
Data Modeling Sensitive Data
Streams
Aggregated
Data streams
Data Contributors Trusted Aggregator
Privacy Preserving Feedback Control
Application
Aggregation
Perturbation
Prediction
Correction
Cloaking
Collection
Next Generation DDDAS
• Privacy-preserving, secure acquisition High-performance
• Fusion/aggregation of uncertain data secure distr. comp.
• Prediction/correction/application steering + feedback loop
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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
DDDAS Secure Tasking
Mobile participants/sensors: feedback + prediction
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)
DDDAS Enhanced Cloaked Tasking
• TC: Sum of participant to assigned-target distancesTU: Sum of valid assignments/target normalized to requiredPC: Penalized cost – sum of cost + uncovered penalty
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 = {s1, s2, … sv} or ∅ (missing)
• Determine “state label” at location lj at time tk
Truth Inference Approach
• Hidden Markov Model using iterative approach to
determine transition probabilities
• Challenges: methods for other aggregation/
fusion/assimilation functions with uncertain data
• Algorithm summary
• Initial guess history + heuristics
• Seek max posterior probability
• Semi- and un-supervised learning
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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)
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Security Schemes – Experiments
• Secure sum protocols in different schemes for nparticipants.
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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
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
Thank you • Acknowledgements
• AFOSR DDDAS FA9550-12-1-0240
• Project team
• Investigators: Li Xiong, Vaidy Sunderam
• Students: Liyue Fan, Slawek Goryczka, Layla Pournjaf, Daniel
Garcia-Ulloa, Xiaofeng Xu
• Project URL
• http://www.mathcs.emory.edu/predict/
AFOSR DDDAS FA9550-12-1-0240