my data store - telecom italia
TRANSCRIPT
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
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
My Data Store:
The User Digital «shadow/footprint»
Localization
Communications
&Interactions
Consumptions (e.g. Utilities)
Payments
Health & WellnessRetailing
Social Network
Personal Data Stores
Awareness
Personal Data Store Social Value
(Personal Big Data)
Exploitation
– Disclosure
Apps, Services, ...
Control
Collects
PD from
Heterogenous
sources
User-CentricPersonal Data Management
My Data Store –
Reference case at the
World Economic Forum 2013
My Data Store
Main features
Web
Mobile
10
My Data Store: data types & data connectors
Social
Networks
Wearable &
Wellness
Expenses
Utilities
Mobile (Apps., etc…)
Retail/GDO
Localization
Communication
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
12
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
PERSONAL DATA PLATFORM
My Data Store: App.s ecosystem
One Personal Data Management platform
enabling many application scenarios
The MTL ecosystem of co-designed Trusted Applications
TIM
CheckApp
Personal
Money Mng.
Mobile
My PDS
Familink Favour
ExchangeSecondNose
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
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
Control and Exploitation featuresUser primacy over the entire PD life-cycle (from collection to usage)
Deletion Area
Sharing Area
Collection Area
Control and Exploitation featuresData exploitation with trusted apps, controlling apps’ behavior
Trusted Apps
Types of Data
Types of Usages
App Privacy Prefs
Increasing Awareness and Engagement and stimulate toward the Exploitation of PD
Aggregated Individual Views(charts, timelines, maps, clusters, …)
Detailed «Auditing» Views(raw/single data)
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