My Data Store - Telecom Italia
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Transcript of My Data Store - Telecom Italia
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Trusted Data Management with Service Ecosystem – EIT ICT Lab’s HII
My Data Store presentationEuropean Trusted Cloud Platform
with Advanced Data and Service Management Ecosystem
Interactive future cloud clinic event in London
Digital Catapult Centre – London (UK), January, 19th 2015
Michele Vescovi
Telecom Italia – SKIL
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Smartphone
SME
SECURE STORAGE / Filtering / Aggregation / Anonymization
Personal Data from
Smartphone Sensors
Personal Data from Apps and Services
Territorial, Business,
Organization Data
Personal Data Store
PDS
Access Policies
Policy enforcement
APIs...
EncriptionAnalytics
Visualizations
Businesssupport &
Intelligence
Services(APPs / WEB)
Territorial, Business,
Organization Data
Container
Users: produce different types of Personal
Data, set access policies, access PDS services andadded value APPS, control monetization, etc.
Organizations: produce data, benefit
from aggregations with other data sourcesand from add value APPs/Services on their data
Data Stream
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My Data Store:
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The User Digital «shadow/footprint»
Localization
Communications
&Interactions
Consumptions (e.g. Utilities)
Payments
Health & WellnessRetailing
Social Network
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Personal Data Stores
Awareness
Personal Data Store Social Value
(Personal Big Data)
Exploitation
– Disclosure
Apps, Services, ...
Control
Collects
PD from
Heterogenous
sources
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User-CentricPersonal Data Management
My Data Store –
Reference case at the
World Economic Forum 2013
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My Data Store
Main features
Web
Mobile
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10
My Data Store: data types & data connectors
Social
Networks
Wearable &
Wellness
Expenses
Utilities
Mobile (Apps., etc…)
Retail/GDO
Localization
Communication
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Smartphone
SME
SECURE STORAGE / Filtering / Aggregation / Anonymization
Personal Data from
Smartphone Sensors
Personal Data from Apps and Services
Territorial, Business,
Organization Data
Personal Data Store
PDS
Access Policies
Policy enforcement
APIs...
EncriptionAnalytics
Visualizations
Businesssupport &
Intelligence
Services(APPs / WEB)
Territorial, Business,
Organization Data
Container
Users: produce different types of Personal
Data, set access policies, access PDS services andadded value APPS, control monetization, etc.
Organizations: produce data, benefit
from aggregations with other data sourcesand from add value APPs/Services on their data
Data Stream
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Smartphone
Storage/Encryption/Retrieval
Personal
Data from
APPS
PDS
User
control and
awareness
User
Community
management
(Industry,
Research)
Data Stream
Mobile
Personal Data
Store
Personal
Data from
Services
(e.g. WEB)
API for(Personal/
Social/...)
APPSAuditing
Data Quality
Assessment
Connector
s to
external
data
AuthenticationDASHBOARD
CB
EXPLORATIO
N
Filtering / Aggregation / Anonymization /
Semantic Engine
My Data Store: Architecture
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PERSONAL DATA PLATFORM
My Data Store: App.s ecosystem
One Personal Data Management platform
enabling many application scenarios
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The MTL ecosystem of co-designed Trusted Applications
TIM
CheckApp
Personal
Money Mng.
Mobile
My PDS
Familink Favour
ExchangeSecondNose
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The platform aggregates
data from all the sensors
each collecting a data point
every five minutes.
An app provides users real time
information about the
collected locations indicating
the quality of the air breathed
during a day.
DAILY INDIVIDUAL MAP
DYNAMIC COLLECTIVE MAP OF THE CITY
Example: SecondNose (air Monitor)
SecondNose
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My Data Store mobile: dinamically control apps behavior
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Application opportunities on top of My Data Store’s data
User awareness, quantified self, …360°
User/Social Smart City data-driven services
Trusted apps for exploit/disclose
personal data into services…
Personal (BIG) Data in TI
Data Monetization
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Control and Exploitation featuresUser primacy over the entire PD life-cycle (from collection to usage)
Deletion Area
Sharing Area
Collection Area
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Control and Exploitation featuresData exploitation with trusted apps, controlling apps’ behavior
Trusted Apps
Types of Data
Types of Usages
App Privacy Prefs
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Increasing Awareness and Engagement and stimulate toward the Exploitation of PD
Aggregated Individual Views(charts, timelines, maps, clusters, …)
Detailed «Auditing» Views(raw/single data)
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Social Views(collaborative views, comparison, …)
Aggregated Individual Views(charts, timelines, maps, clusters, …)
Detailed «Auditing» Views(raw/single data)
Increasing Awareness and Engagement value for the community & social comparison
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Trusted Data Management with Service Ecosystem – EIT ICT Lab’s HII
My Data Store presentationEuropean Trusted Cloud Platform
with Advanced Data and Service Management Ecosystem
Interactive future cloud clinic event in London
Digital Catapult Centre – London (UK), January, 19th 2015
Michele Vescovi
Telecom Italia – SKIL