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PDT (™): Personal Data from Things,and its provenance

Paolo MissierSchool of Computing Science

Newcastle University

The SRC-IoT Workshop:Systems Research Challenges in the Internet of Things

Northumberland, January 11-12, 2016

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The Internet of Things is Many Things

The IEEE IoT initiative

Revision 1– 27 MAY 2015

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One of the possible stacks

Source: Towards a definition of the Internet of Things (IoT) IEEE Internet Initiative Iot.ieee.orgTelecom Italia S.p.A. Roberto Minerva, Abyi Biru, Domenico Rotondi, May 2015

Metadata

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It’s all about connectivity

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Evolution of the Internet (according to ETSI)

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Evolution of the Internet (according to Google)

information graph    connection to contentsocial graph  connections amongst peoplephysical graph connections amongst things

Source: IEEE Internet of Things Vint Cerf, Google - December 15th 2015

“we’ll have devices that are more aware of us

than we are of them”

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Use cases – at different scales

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IoT and Smart-*

50 Sensor Applications for a Smarter World

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The connected washing machine example

Source: IEEE Internet of Things Vint Cerf, Google - December 15th 2015

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Role of metadata and provenance for IoT: three angles

• IoT ∩ Science

• IoT ∩ People Personal Data from Things (PDT)

• Things that make decisions

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IoT ∩ Science

Sensor-based science- Pervasive / ubiquitous computing,

human/animal behaviour analysis, climate science, …

Some well known issues:- Sensor reading quality – QA, outliers, false readings- What we have: Metadata / context

- About the sensors id, type, calibration, parameter settings- About the data readings timestamp- About the quality assessed through QA processes

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IoT ∩ Science Metadata

This requires capturing and managingprovenance and other metadata

Provenance: a record of data derivation through multiple process transformations

- Complementary to descriptive metadata- enables reasoning about the findings, validation

• How was the data collected?• How was it processed?• Who was responsible?

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PROV

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And its human-readable, formal representation

prefix ex <http://example.org/>

// what happened?entity(ex:docDraft, [ prov:type="paper", ex:version="v.01", ex:status="draft" ])activity(ex:drafting, 2013-03-16T10:00:00, 2013-03-17T10:00:00)wasGeneratedBy(ex:docDraft, ex:drafting, 2013-03-18T10:00:01)entity(ex:paper1, [ prov:type="paper", ex:doi="..."])entity(ex:paper2, [ prov:type="paper", ex:doi="..."])used(ex:drafting, ex:paper1, -)used(ex:drafting, ex:paper2, -)

// who was responsible?agent(ex:Bob, [ ex:firstName="Robert", ex:lastName="Thompson", prov:type="ex:seniorEditor" ])//agent(ex:Alice, [ ex:firstName="Alice", ex:lastName="Cooper", prov:type="ex:chiefEditor" ])

wasAssociatedWith(ex:drafting, ex:Bob, -) // no plan

// delegationactedOnBehalfOf(ex:Bob, ex:Alice) // global activity scope

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Provenance pattern for sensor data

Key issue: managing data/process granularityVolume, complexity of transformations P1, P2, …. black/grey/white box provenance

- how much detail do we need?

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IoT ∩ Science

Typical uses for provenance:• impact analysis (forwards)• cause analysis (backwards)

Note on reproducibility: Observational data is generally not reproducible!

How much provenance is needed?

Impact analysis:Suppose a sensor is later determined to be faulty (false readings)How does that impact the experimental findings?

Cause analysis:These conclusions seem implausible. What went wrong along the process?

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IoT ∩ People Personal Data from Things (PDT)

IoT vision: devices (“smart washing machine”) will make our lives better

They often also produce data that is also personalAs per the Data Protection Act 1998

• Are people aware of the trade-offs between privacy and benefits?

1. Ownership:• What is “my” data? (who owns the utility consumption figures in my

house? Or an activity trace collected using a “smart shoe”?)• Who else has access to it? To what extent?

2. Awareness of third party use of personal data: • Who has been doing what with my data?• How much of the data used in a certain computation is my data?? • What has its contribution been to the analytics?

3. Control. How much control can I have on the data that devices produce on my behalf?

Ownership + awareness + control Trust

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Two recent publications

Mashhadi, Afra, Fahim Kawsar, and Utku Gunay Acer. “Human Data Interaction in IoT: The Ownership Aspect.” In Internet of Things (WF-IoT), 2014 IEEE World Forum on, 159–162, 2014.

Vescovi, Michele, Corrado Moiso, Fabrizio Antonelli, Mattia Pasolli, and Christos Perentis. “Building an Eco-System of Trusted Services through User Transparency, Control and Awareness on Personal Data Privacy.” In Procs. W3C Workshop on Privacy and User–Centric Controls. Berlin, Germany, 2014.

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IoT ∩ People Personal Data from Things (PDT)

Example:SPHERE - a Sensor Platform for HEalthcare in a Residential Environment(EPSRC, 2013-2018, Bristol, Prof. Ian Craddock) http://irc-sphere.ac.uk/

Zhu. N, Diethe. T, Camplani. M, Tao. L, Burrows. A, Twomey. N, Kaleshi. D, Mirmehdi. M, Flach. P, Craddock. I, Bridging eHealth and the Internet of Things: The SPHERE Project. IEEE Intelligent Systems 30 (4), 39-46. (doi: 10.1109/MIS.2015.57)

All about sensing, wearables, & detecting people’s activitiesInstrumented “SPHERE house” — scaling up to 100 homes by 2017 lots of data collection, data mining challenges

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Activity detection pattern

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Activity detection: provenance pattern

Key issue:Distributed, fragmented provenance

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Identity management

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IoT Standards –smart objects

Smart objects identity and privacy

Source: IoT Standards: The Next Generation

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IoT is really about M2M!

Example: V2V (Vehicle-to-Vehicle coordination) And the IoV (Internet of Vehicles)

Source: Mario Gerla, "Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds”, IEEE IoT forum, Dec. 2015, Keynote

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What is M2M?

Data communication among the physical things which do not need human interaction.

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Things that make decisions

Some challenges:Provenance patterns for streaming, message passing: “V1 sent sij to V2”How much “provenance” does each sensor reading need to carry? How does this fit with M2M protocols?Provlets: embed in messages vs stored separately in a repository

(indexed by key: <S.id, t>)

- M2M means more in-network provenance- The data remains at the edge of the network

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Metadata management in the IoT architecture – oneM2M model

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SenML

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Fog and Cloud

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Cloud vs Fog computing

ref.: Datta, S.K.; Bonnet, C.; Haerri, J., "Fog Computing architecture to enable consumer centric Internet of Things services," in Consumer Electronics (ISCE), 2015 IEEE International Symposium on, pp.1-2, 24-26 June 2015

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Key points for provenance in the IoT context

Provenance for M2M at the edge

• Embedding / associating metadata with M2M messages

• Generating provlets in a Fog architecture• Reconstructing a coherent provenance graph from the fragments

• Provenance / metadata analysis in the cloud