Big Data and SecurityMichel Burger
0.05 ounces/ton                  Gold mining is about dirt management
About 11850 Amps to generatearound 8.4 Tesla fields (about   150000 times the earth   magnetic field) but they   operate a...
How BIG?BIG data is like the LHC combined with goldextraction- Huge amount of data -> 6.6 Zettabytes/year by 2016 (Cisco  ...
The essence of new serviceproviders                                                                 BI Based Revenue Model...
Classic Approach• Structured Data• Data in the range of Gigabytes to Terabytes• Centralized (Data is imported in analytics...
Big Data Approach  • Multi Structured Data  • Data in the range of Terabytes to Petabytes  • Distributed/Federated (Analyt...
A new pattern             • Many different data structures             • Many different ways to extract the data          ...
With added security                                                                Knowledge                              ...
Final thoughts1. We need to eliminate the silos   – Sources or Usage2. Still very much a collection of technologies   – Th...
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Vodafone xone fev142013v3 ext

  1. 1. Big Data and SecurityMichel Burger
  2. 2. 0.05 ounces/ton Gold mining is about dirt management
  3. 3. About 11850 Amps to generatearound 8.4 Tesla fields (about 150000 times the earth magnetic field) but they operate at low Voltage A lot of what LHC is about is electricity flow management
  4. 4. How BIG?BIG data is like the LHC combined with goldextraction- Huge amount of data -> 6.6 Zettabytes/year by 2016 (Cisco Cloud Index)- Big flow of data -> 400TB/day (Facebook)- LHC generates 10-15 Petabytes/year of data for each experiment
  5. 5. The essence of new serviceproviders BI Based Revenue Models (eg Advertisement) User Core Semantic Improves Consumes experience Data Set Mindmap Revenue fromValue enriched Data existing services generates revenue Data Service will shrink Service Produces Service Additional revenue from new services The more contextthe more efficient and One data set Many free services the more value and common semantic Example: Search/Information Management : Rated auction/Selling:
  6. 6. Classic Approach• Structured Data• Data in the range of Gigabytes to Terabytes• Centralized (Data is imported in analytics)• Batch based• Data silos ETL ETL ETL Transaction Relational Data Analyse Database Warehouse Where is the data that answer my questions ?
  7. 7. Big Data Approach • Multi Structured Data • Data in the range of Terabytes to Petabytes • Distributed/Federated (Analytics grab the data) • Streaming based • Holistic Data Clusters 1 Stream 2 Organize Analyse 3 nHere are the questions and the data for the answers
  8. 8. A new pattern • Many different data structures • Many different ways to extract the data Knowledge • Structured • Many different locations (even for the References API Services ContentSources Applications Social Networks Buffering same type of data) • Proprietary • RAN Graph • Batch and Realtime based Data card • Data as a Service Neural Network • Buffered or stream Sim Card Premise Network Core • Connected Things Connected (Consumer, Enterprise) Gateway Relational • Correlation parameters • Unstructured Devices IT Infrastructure Consumption Buffering Report Statistics • Streaming • Taping at Source Real-time Cheap Storage High Efficient Storage Low level Semantic • Buffering, Routing, Filtering • Taping on Stream • Structured/Unstructured • Consumption to Stream Graph Network/ store Source Analysis • Event Collector • Batch Process/Multi Non Real-time Rich Semantic Structure Stream • Multi Stage Store/Process Neura l Network/ Analysis
  9. 9. With added security Knowledge References API Services ContentSources Social Networks Applications • Strong access RAN control based Data as a Service Data card Sim Card Connected Things (Consumer, Enterprise) Premise Gateway Network Core on industry Connected Devices IT Infrastructure standard Consumption (user, dev, app lication) Report Statistics • Securing the infrastructure (public, private) • Strong • Policy (internal/external) authorization • On-going assessment (DDOS, Penetration …) control based • Data leakage • on open Stream Migration Graph standard Network/ • Securing the identity Analysis • Validating ID • Analytics • Anonymization applied to • Securing the access Analytics • Distributed permission/preference • 3rd party permission Neura l Network/ Analysis
  10. 10. Final thoughts1. We need to eliminate the silos – Sources or Usage2. Still very much a collection of technologies – The assembly is still very complex3. Is everything about events?4. We need to handle the CAP theorem more appropriately5. What is the user experience (not just the end user but also the admin)
  11. 11. Thank You

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