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Privacy Enforcing Algorithms for Distributedand Resource Constrained Power Sharing
Systems
Pacome Landry AMBASSA
PhD StudentDepartment of Computer Science
University of Cape Town; South AfricaEmail: [email protected]
Talk to Internet Technologies and Systems Research GroupHasso Plattner Institut; Potsdam, Germany
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 1 / 29
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Outline
1 Introduction
2 Research ChallengesPrivacy and TrustPower Network Monitoring
3 Micro-grid ArchitectureMicro-grid Framework
4 Power Network MonitoringPreliminary result: Collection of Household Power ConsumptionDataNoise Modeling in Power Consumption DataCurrent Work: Modeling and collection of Household Powerconsumption
5 Conclusion
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 2 / 29
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Introduction
Outline
1 Introduction
2 Research ChallengesPrivacy and TrustPower Network Monitoring
3 Micro-grid ArchitectureMicro-grid Framework
4 Power Network MonitoringPreliminary result: Collection of Household Power ConsumptionDataNoise Modeling in Power Consumption DataCurrent Work: Modeling and collection of Household Powerconsumption
5 Conclusion
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 3 / 29
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Introduction
Introduction — Energy Access in Africa
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Introduction
Introduction
Community that does not have reliable access to electricity
Not connected to the national power gridAccess negatively influenced by load shedding
Governments, private developers and NGOs could provide a localcommunity with a Micro-grids for reliable and equitable access toenergy services.Micro-grids combine local generation (based on renewable energysources including PV or wind) and network management
Distributed generation (mix smaller scale and private generator)Generated capacity may not allow satisfaction of all demand
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 5 / 29
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Introduction
Introduction
Solution: Smart Micro-gridSmart grid system deployed in higher income areas relies on:
Unlimited computation capabilityHigh degree of network reliabilityLarge scale data collectionUsing smart appliances or smart meter
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Introduction
How we Think that it Can Work ?
Resource Constrained Environments
Intermittent bandwidthLimitation of computing technologyUnstable connectivityUnstable power connection
Re-modeling the micro-grid architecture
Incorporate low cost information andcommunication technology
Mobile computing devices
Sensors
Wireless communication technology.P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 6 / 29
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Introduction
What is Considered as Data?
Figure 1: Household power profile [Quinn,2009]P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 7 / 29
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Research Challenges
Outline
1 Introduction
2 Research ChallengesPrivacy and TrustPower Network Monitoring
3 Micro-grid ArchitectureMicro-grid Framework
4 Power Network MonitoringPreliminary result: Collection of Household Power ConsumptionDataNoise Modeling in Power Consumption DataCurrent Work: Modeling and collection of Household Powerconsumption
5 Conclusion
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 8 / 29
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Research Challenges Privacy and Trust
Problem # 1: Privacy
1 Proliferation of mobile device as computing device generates largeamount of personal & critical data,
2 This data can be used to profile individual and their behaviors:jeopardize privacy
Usage of electrical appliances and devicesWhat appliances you use, when, e.g. dryer, toaster, microwave,television
Individuals habits or daily routineWhen residents take their breakfast, leave or return home
Lifestyle of householdersHow many people lives in the household; when they are present orawake
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 9 / 29
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Research Challenges Privacy and Trust
Problem # 2: Detecting and preventing power theft
Limited trustworthiness and unreliability of the network devices
Inexpensive sensor with limited trust for monitoringMobile device controlled by user for data collection
Mechanism to prevent fraud and energy theft
Unreliability of the network can make it difficult to distinguishing betweenadversarial and legitimate fault
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 10 / 29
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Research Challenges Power Network Monitoring
Problem #3 : Monitoring the power network
Monitoring contribute to:
Determine power consumptionEnsure state estimation
Provide user control over their energy consumption
Design algorithms capable of providing power consumption estimationon device with limited resource .
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 11 / 29
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Research Challenges Power Network Monitoring
Attacks Classification
Attacks
Intentional modification
External attacks Internal attacks
Collusion of users
Unintentional modification
Measurement errors Power fluctuations
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 12 / 29
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Research Challenges Power Network Monitoring
Attacks Classification
Attacks
Intentional modification
External attacks Internal attacks
Collusion of users
Unintentional modification
Measurement errors Power fluctuations
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 12 / 29
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Research Challenges Power Network Monitoring
Attacks Classification
Attacks
Intentional modification
External attacks Internal attacks
Collusion of users
Unintentional modification
Measurement errors Power fluctuations
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 12 / 29
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Micro-grid Architecture
Outline
1 Introduction
2 Research ChallengesPrivacy and TrustPower Network Monitoring
3 Micro-grid ArchitectureMicro-grid Framework
4 Power Network MonitoringPreliminary result: Collection of Household Power ConsumptionDataNoise Modeling in Power Consumption DataCurrent Work: Modeling and collection of Household Powerconsumption
5 Conclusion
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 13 / 29
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Micro-grid Architecture Micro-grid Framework
Proposed Micro-grid Framework
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Micro-grid Architecture Micro-grid Framework
Power Grids Model
Power NetworkTree topology with branches to each house
Communication Network ModelHierarchical with three layered architectures:
L1 Household devices connected to gateways mobile devices.Devices communicate via wireless communication technologies(WIFI,ZigBee)
L2 Grouping: meters connected to collectors (gateways). Aggregatedata from few houses in the neighborhood.
L3 Network at the micro grid level: connection of aggregate point atthe micro-grid level.
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 15 / 29
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Power Network Monitoring
Outline
1 Introduction
2 Research ChallengesPrivacy and TrustPower Network Monitoring
3 Micro-grid ArchitectureMicro-grid Framework
4 Power Network MonitoringPreliminary result: Collection of Household Power ConsumptionDataNoise Modeling in Power Consumption DataCurrent Work: Modeling and collection of Household Powerconsumption
5 Conclusion
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 16 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
Modeling and collection of Household Power consumption
Ambassa, Kayem, Wolthusen and Meinel, “Secure and Reliable PowerConsumption Monitoring in Untrustworthy Micro-grids", In Proc., InternationalConference on Future Network System and Security (FNSS,2015), June 11-13,2015, Paris, France (To Appear)
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
System Description
Let A the set of all appliances within the house, n = |A |.Aj the set of active devices, Aj ⊆ A and j ∈ [1,p].
s1,s2, . . . ,sn set of sensors embedded into each home appliancesto monitor power consumption.
M mobile device represents sink/aggregation point
Network modelsystem can be modeled by an undirected and connected graphG = (S,E), where S is the set of nodes in the networks and E is aset of communication links among the nodes in S
G is the communication graph of this WSN.
Two nodes si and sj are connected if and only if si communicatesdirectly with sj . si and sj are neighbors
The set N (si) is the set of vertices adjacent to si .
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 18 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
System Description
Let A the set of all appliances within the house, n = |A |.Aj the set of active devices, Aj ⊆ A and j ∈ [1,p].
s1,s2, . . . ,sn set of sensors embedded into each home appliancesto monitor power consumption.
M mobile device represents sink/aggregation point
Network modelsystem can be modeled by an undirected and connected graphG = (S,E), where S is the set of nodes in the networks and E is aset of communication links among the nodes in S
G is the communication graph of this WSN.
Two nodes si and sj are connected if and only if si communicatesdirectly with sj . si and sj are neighbors
The set N (si) is the set of vertices adjacent to si .P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 18 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
Challenges
Determination of household power consumption under the followingconditions:
1 Lack of globally shared clock between different nodes(synchronization problems)
2 Unpredictable communication latency3 Power consumption values are spread across several appliances4 Nodes susceptible to failure (crash and malfunction)5 The presence of network adversaries : (data modification attack,
denial of service attacks)
ProblemDesign an efficient protocol for collection of power consumption data inhome power networks.
Similar to the computation problem in Distributed System: Global Statecollection
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 19 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
Challenges
Determination of household power consumption under the followingconditions:
1 Lack of globally shared clock between different nodes(synchronization problems)
2 Unpredictable communication latency3 Power consumption values are spread across several appliances4 Nodes susceptible to failure (crash and malfunction)5 The presence of network adversaries : (data modification attack,
denial of service attacks)
ProblemDesign an efficient protocol for collection of power consumption data inhome power networks.
Similar to the computation problem in Distributed System: Global Statecollection
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 19 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
Challenges
Determination of household power consumption under the followingconditions:
1 Lack of globally shared clock between different nodes(synchronization problems)
2 Unpredictable communication latency3 Power consumption values are spread across several appliances4 Nodes susceptible to failure (crash and malfunction)5 The presence of network adversaries : (data modification attack,
denial of service attacks)
ProblemDesign an efficient protocol for collection of power consumption data inhome power networks.
Similar to the computation problem in Distributed System: Global Statecollection
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 19 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
Snapshot Algorithm: A solution for Global State
The Snapshot produce a global state of a DSCollection of local states of process Pi .Collection of the communication channel state .
The state of process Pi is the content of processors, register, stackand memoryThe state of the channel is characterize by the set of message intransit
A global state corresponds to the “picture" of the home appliancesenergy consumption transmitted to mobile devices to evaluatehousehold’s energy consumption.
Model of communication¶ Non FIFO channel: random order
· FIFO channel: First In First Out ordering
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
Snapshot Algorithm: A solution for Global State
The Snapshot produce a global state of a DSCollection of local states of process Pi .Collection of the communication channel state .
The state of process Pi is the content of processors, register, stackand memoryThe state of the channel is characterize by the set of message intransit
A global state corresponds to the “picture" of the home appliancesenergy consumption transmitted to mobile devices to evaluatehousehold’s energy consumption.
Model of communication¶ Non FIFO channel: random order
· FIFO channel: First In First Out ordering
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
The Proposed Algorithm
Marker: the control message that informs the sensor node torecord the value(s) measured. It contains: sid , the ID of the sendernode; and snapnumb, the snapshot number.
Feedback: the message sent by a sensor to the sink node. Itcontains: sid , identifier of the sender node; Nsnd , the new valuerecorded; snapnumb an integer which indicates the snapshot; andMid , the ID of the sink node.
lmd : a real number which is the reading of the sensor at a givenpoint in time.
Osnd : the old value collected in the previous snapshot
flag: A Boolean value that indicates if a sensor node has receivedthe marker.
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
The Proposed Algorithm
¶ Creation of spanning tree for communication· Three steps algorithm:
ä Snapshot initiationä Reception of Markerä Feedback response
Phase 1: Snapshot initiationThe mobile device broadcast Marker (sid ,snapnumb) over a spanningtree initiate the collection
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
The Proposed Algorithm
¶ Creation of spanning tree for communication· Three steps algorithm:
ä Snapshot initiationä Reception of Markerä Feedback response
Phase 2: Reception of MarkerUpon receiving the marker message, Marker (sid ,snapnumb), thereceiver (an adjacent neighbor sj ∈ N (si) first check the flag value.
If the value of flag is false, sj has not yet received the marker thenit records its current readings lmd .
sj broadcast the control message Marker (sj ,snapnumb) to itsadjacent neighbor.
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 22 / 29
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Power Network Monitoring Preliminary result: Collection of Household Power Consumption Data
The Proposed Algorithm
¶ Creation of spanning tree for communication· Three steps algorithm:
ä Snapshot initiationä Reception of Markerä Feedback response
Phase 3: Feedback response
If Nsnd 6= Osnd send Feedback with (sid ,Nsnd ,snapnumb,Mid ) .
Osnd ← Nsnd .
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Power Network Monitoring Noise Modeling in Power Consumption Data
Noise in Power Data
Noise comes from multiple sources:Errors from the physicalmeasurement and Malicious tampering with measurement
1 Errors from the physical measurement (measurement errors):The difference between the measured value and the true valueLet u be the true value, x be the measured value and β be themeasurement error. Then, β = x−u or u = x−β.Three different types of measurement errors: systematic errors,random errors and negligent errors
2 Malicious tampering with measurement: data injection:Random false data injection attacksTargeted false data injection attacks
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Power Network Monitoring Noise Modeling in Power Consumption Data
Measurement Errors
1 Systematic errors
Result from imperfections of the metering equipment, inexactadjustment and pre-settingsNo statistical techniques to quantify systematic errors[Hughes,2010]
2 Random errors
The reading of si taken at different time fluctuates.The combination of such tiny perturbations is represented as arandom variable XX follow Gaussian distributions.
3 Negligent errors
Result from mistakes or a malfunction of the measuring device
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Power Network Monitoring Noise Modeling in Power Consumption Data
Malicious tampering with measurement: data injection
Maliciously inject erroneous into the data stream in order tomisreport consumption
Two types of false data injection attacks [Liu,2011]
Random data injection attacks
Targeted data injection attacks.
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Power Network Monitoring Current Work: Modeling and collection of Household Power consumption
Current Work: Modeling and collection of Household Powerconsumption data in the presence of adversaries
Modeling electrical consumption of the entire household based onthe operational characteristic of appliances
Robust and efficient snapshot algorithm that can tolerates randomfailure and adversary attack
Node non respondentLink failureMessage lost, suppress
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Conclusion
Outline
1 Introduction
2 Research ChallengesPrivacy and TrustPower Network Monitoring
3 Micro-grid ArchitectureMicro-grid Framework
4 Power Network MonitoringPreliminary result: Collection of Household Power ConsumptionDataNoise Modeling in Power Consumption DataCurrent Work: Modeling and collection of Household Powerconsumption
5 Conclusion
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Conclusion
Conclusion
Most of our daily activity are electricity dependent
Privacy and trust in power grids are major problems
We proposed:
Framework for a cost efficient micro grid architecture for powerdistribution in constrained resource environmentDistributed snapshot algorithm for power consumption collection inan asynchronous and distributed networkModel of noise in data collection
P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 28 / 29