Linked Data for a privacy-aware Smart Grid

INSTITUTE OF APPLIED INFORMATICS AND FORMAL DESCRIPTION METHODS† AND ZENTRUM FUR ANGEWANDTE RECHTSWISSENSCHAFT‡

Linked Data for a privacy-aware Smart Grid

Andreas Wagner†, Sebastian Speiser†, Oliver Raabe‡ and Andreas Harth† | INFORMATIK 2010

KIT – University of the State of Baden-Wuerttemberg and

National Laboratory of the Helmholtz Association

www.kit.edu

Project MeRegioMobil

This work was supported by the German Federal Ministry of Economicsand Technology (E-Energy MeRegioMobil, Grant 01ME09005). Theauthors are responsible for the content of the presentation.

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Agenda

1 Introduction

2 Communication Architecture and Data ModelA Semantic Web-based Communication ArchitectureLinked RDF Data for the Smart Grid

3 Policies for a Privacy-aware Smart GridPolicy ModelPolicy-aware Data Access

4 Evaluation via Privacy Principles

5 Conclusion and Future Work

6 References

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Smart Grid Basics

What is the Smart Grid?The Smart Grid is a radical redesign of the ageing energy grid, which aimsat profoundly changing the way how energy is created, distributed andconsumed and promises to save considerable amounts of energy [1, 2].

ICT as a Smart Grid enablerSmart Grid includes a communication layer.

There are many information between many actors.

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Smart Grid Basics

What is the Smart Grid?The Smart Grid is a radical redesign of the ageing energy grid, which aimsat profoundly changing the way how energy is created, distributed andconsumed and promises to save considerable amounts of energy [1, 2].

ICT as a Smart Grid enablerSmart Grid includes a communication layer.

There are many information between many actors.

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Smart Grid Overview

Figure: Smart Grid Overview [2]

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Smart Grid Challenges

Resulting ChallengesAllow data integrationbetween various actors.

Enforce data privacywithin Smart Grid.

Figure: Smart Grid Overview [2]

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Contribution

Web-based communication architecture→ Section 2(Semantic) Web technologies as a suitable communication architecture.Linked RDF as data model.

Policy model→ Section 3, 4A policy model for expressing and enforcing privacy restrictions. Couplingof policies and published information.

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Contribution

Web-based communication architecture→ Section 2(Semantic) Web technologies as a suitable communication architecture.Linked RDF as data model.

Policy model→ Section 3, 4A policy model for expressing and enforcing privacy restrictions. Couplingof policies and published information.

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Overview of a Semantic Web-basedCommunication Architecture I

Figure: Semantic Web Stack

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Overview of a Semantic Web-basedCommunication Architecture II

Data access layersURIs for identification of participants.

TCP/IP stack with HTTP as transfer protocol.For low-power devices, e.g., a light-weight layered architecture:

IEEE 802.15.4 (physical and MAC layer).6LoWPAN (internet layer, IPv6 version for IEEE 802.15.4 networks).

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Overview of a Semantic Web-basedCommunication Architecture III

Data representation layersRDF(S) (if necessary extended with OWL features) formachine-interpretable data encoding.Linked Data principles for data publishing and integration:

Use (HTTP) URIs for identification of entities.When someone looks up a URI, provide useful (RDF) data.Include links to other URIs.

Application layersProof and trust mechanisms for privacy and security.

SPARQL as means for querying RDF data.

. . .

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Overview of a Semantic Web-basedCommunication Architecture III

Data representation layersRDF(S) (if necessary extended with OWL features) formachine-interpretable data encoding.Linked Data principles for data publishing and integration:

Use (HTTP) URIs for identification of entities.When someone looks up a URI, provide useful (RDF) data.Include links to other URIs.

Application layersProof and trust mechanisms for privacy and security.

SPARQL as means for querying RDF data.

. . .

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Linked RDF Data for the Smart GridMary’s Linked Data Example:

Customer

Electric Vehicleex:uamp760e

Smart Meterex:sm

Washing Machineex:coolWash

Premiseex:apt

ex:Mary

Energy EfficiencyService Provider

Metering Provider

Cool WashWeatherData

UsageStatistics

Figure: Mary’s Linked Data

Obligatory and non-obligatory dataObligatory data: data associated with legal consequences (e.g.,billing). Data is managed by a trusted party, e.g., a metering provider.

Non-obligatory data: all other data. Data is managed by the device(e.g., car) or a gateway actor (e.g., smart meter).

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Linked RDF Data for the Smart GridMary’s Linked Data Example:

Customer

Electric Vehicleex:uamp760e

Smart Meterex:sm

Washing Machineex:coolWash

Premiseex:apt

ex:Mary

Energy EfficiencyService Provider

Metering Provider

Cool WashWeatherData

UsageStatistics

Figure: Mary’s Linked Data

Obligatory and non-obligatory dataObligatory data: data associated with legal consequences (e.g.,billing). Data is managed by a trusted party, e.g., a metering provider.

Non-obligatory data: all other data. Data is managed by the device(e.g., car) or a gateway actor (e.g., smart meter).

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Linked RDF Data for the Smart Grid II

Mary’s Linked Data

/ / lookup on ex : coolWash ; data res ides a t washing machineex : coolWash

r d f : type sg : Appl iance ;sg : manufacturer <h t t p : / / coolWash . com/ company>;sg : owner ex : mary ;sg : washingData washer : program40 ;sg : consumption sm: data20100310 .

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Linked RDF Data for the Smart Grid III

Mary’s Linked Data II

/ / lookup on sm: data20100310 ; data res ides a t smart metersm: data20100310

r d f : type sg : Consumption ;r d f : value ” 1 . 0 4 ” ˆ ˆ sg :kWh;i c a l : d t s t a r t ”2010−03−10T00 : 0 0 : 0 0 ” ;i c a l : dtend ”2010−03−10T01 : 0 0 : 0 0 ” .

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Policy Model

IntuitionPolicies model user intent→ help to preserve data privacy. A Policy isbound to its associated data. Policies are taken into account wheneverdata is accessed.

Policy DefinitionA Policy models a timespan during which it is valid via ical:dtstart andical:dtend. A Policy allows a number of usages, which is restricted to aspecific purpose and to a recipient. A perspective restricts the(granted) data access to specific data - it is specified via SPARQL queries.

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Policy Model II

Usage

Purpose

Policy

AgentDescriptionDate Perspective

validFrom validTo perspective

allows

recipientpurpose

Figure: Policy Definition

Private and public policiesPolicies specified by a private party.

Policies specified by law.

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Policy Model II

Usage

Purpose

Policy

AgentDescriptionDate Perspective

validFrom validTo perspective

allows

recipientpurpose

Figure: Policy Definition

Private and public policiesPolicies specified by a private party.

Policies specified by law.

Introduction Communication Architecture Policy Model Evaluation Conclusion References

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Policy Model III

Linked recipient and purposePurpose and recipient are externally defined resources at a trustedsource.

Purpose and recipient are integrated via Linked Data principles.

Solution is similar to the Creative Commons approach.

Any Purpose

NonCommercial Commercial StatisticsConsulting

Billing AdvertisementCommercialConsulting

WelfareConsulting

Figure: Exemplary Purpose Hierarchy

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Policy Model IV

An example policy for Mary’s UltraAmp 760e I

washer : eCarPol r d f : type sg : Po l i cy ;i c a l : d t s t a r t ”2010−01−01T00 : 0 0 : 0 0 ” ˆ ˆ xs : dateTime ;i c a l : dtend ”2010−12−31T23 : 5 9 : 5 9 ” ˆ ˆ xs : dateTime ;sg : a l lows #ultraAmpUse .

#ultraAmpUse r d f : type sg : Usage ;sg : purpose gov : Purpose# serv i ce ;sg : r e c i p i e n t <h t t p : / / ultraAmp .com/ company>;sg : perspec t i ve #ul t raAmpPerspect ive .

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Policy Model V

An example policy for Mary’s Mary’s UltraAmp 760e II

#ul t raAmpPerspect ive r d f : type sg : Perspect ive ;sg : d e f i n i t i o n ”PREFIX . . . CONSTRUCT { ?s ?p ?o }

WHERE { ?s r d f : type sg : Appl iance .?s sg : manufacturer <h t t p : / / ultraAmp .com/ company>.?s ?p ?o .FILTER (?p != sg : consumption ) } ” .

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Policy-aware Data Access I

Access procedurei) Requestor performs a HTTP lookup on a URI (e.g., ex:uamp760e).

ii) Web server returns an authorisation required response.

iii) Requestor sends a request, i.e., a specification of identity andpurpose.

iv) Device matches the request with an applicable policy (either alaw-based or a user policy)→ if request and policy match, requesteddata and (signed) policy is sent.

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Policy-aware Data Access II

Matching procedureThe matching procedure is implemented as a rule, checking whether . . .

i) requestor is subsumed by the recipient description and

ii) the requested purpose is subsumed by the allowed purpose (bothw.r.t. the applicable policy)

Assumption: the same purpose and recipient definition is employed→subclass-of or same-as check is sufficient for realising the subsumeoperation.

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Policy-aware Data Access III

Policy and Request Matching Rules

Law Policies Private Policies

Domain Ontologies(Smart Grid, Appliances)

Taxonomies(Purposes, Agents)

Figure: Dependencies for Policy Matching

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Evaluation via Privacy Principles I

Principle: data economyData economy: use as little personal information as possible.

An ideal system w.r.t. data economy would employ an anonymisationdirectly at the data source.

Early anonymisation is not possible in general, as e.g., consumptiondata is required to have personal data associated (e.g., for billingpurposes).

Employing pseudonyms would satisfy the data economy principle,while allowing, e.g., a regular billing process.

Approach may be extended to incorporate such anonymisationfeatures.

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Evaluation via Privacy Principles II

Principle: purpose limitationPurpose limitation: specifies that information has to be used inaccordance with the purpose it was originally published for.

Approach supports purpose limitation, as requested data is alwaysreleased together with a policy describing the intended purpose.

No mechanism to modify the original purpose later on (assumingpolicy integrity).

One can implement checks for purpose modifications and legitimateusage.

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Evaluation via Privacy Principles III

Principle: transparencyData economy: data may only be used, if the affected person is informedabout the usage details.Fully integrated. Assumption: for each task data is (again) requested→with each lookup, the user is notified about request, purpose and recipient.

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Conclusion and Future Work I

Conclusion(Semantic) Web technologies provide a suitable communicationarchitecture for the Smart Grid.Publishing linked RDF data fosters a privacy-aware Smart Grid.

The policy layer (based on (linked) RDF data) allows users to expresstheir intents.Via a coupling of user data and its associated policy, technicalenforcement of privacy becomes feasible.

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Conclusion and Future Work II

Future workWork on technical enforcement of privacy, i.e., automated privacychecks (at certain actors) in the Smart Grid.

Use machine-interpretable service descriptions.Check what data (maybe employing an a priori data transformation,e.g., anonymisation) can be used for a particular service.Enforce privacy checks at crucial points (e.g., metering provider) in theSmart Grid.

Scalability of our approach; crucial in particular w.r.t. low-powerdevices.

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Questions?

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MeRegioMobil – Project Context I

The eEnergy Initivative:

A program funded by the German Ministry of Economics andTechnology in cooperation with the Ministry for Ecology.

Climate change, the increasing demand for energy, and the scarcityof resources will cause great challenges to Europe. In order to supplyall demand in the public and private sector in an economic andecological way, the eEnergy initiative is supported by privateenterprises and politics.

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MeRegioMobil – Project Context II

Energy efficiency and information technology were rarely linked in thepast. This will change with the “internet of energy”, an innovativeprogram also called eEnergy. Analogously to terms like eCommerceor eGovernment, eEnergy stands for the digitalization andoptimisation of processes in the energy sector along the entire valuechain – from the producer to the consumer. Existing infrastructureshall be optimized and effectively modernized to support theintegration of renewable and distributed energy sources as well asthe reduction of CO2 emissions.

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References I

European Technology Platform - SmartGrids Vision and Strategy forEuropes Electricity Networks of the Future.European Comission, 2006.http://www.ec.europa.eu/research/energy/pdf/

smartgrids_en.pdf.

NIST Framework and Roadmap for Smart Grid InteroperabilityStandards.National Institute of Standards and Technology, 2010.

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