pacome hpi potsdam_presentation

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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


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Introduction

Outline

1 Introduction




5 Conclusion


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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


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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




5 Conclusion


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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


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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


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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 .


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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


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Micro-grid Architecture

Outline

1 Introduction




5 Conclusion


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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.


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Power Network Monitoring

Outline

1 Introduction




5 Conclusion


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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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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 .


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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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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


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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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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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¶ 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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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.


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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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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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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




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


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Thank for your kind attention !!!P. L. AMBASSA (UCT) Privacy in constrained systems 13-05-2015 29 / 29

pacome hpi potsdam_presentation

Science

constrained systems

ambassa uct privacy

trust power network

national power grid

microgrid architecture

introduction outline

research challenges

grid framework