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1. If ramayana can be reduced to one shlok….then can’t I Complete covering “SPATIAL Big DATA & SECURITY “ IN 15 MIN ?

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Presentation on theme: "1. If ramayana can be reduced to one shlok….then can’t I Complete covering “SPATIAL Big DATA & SECURITY “ IN 15 MIN ?"— Presentation transcript:

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2 If ramayana can be reduced to one shlok….then can’t I Complete covering “SPATIAL Big DATA & SECURITY “ IN 15 MIN ?

3 Do lafzon ki hai DATA ki kahani Ek hai ZERO....duja hai ONE

4 Big Data Security WELCOME 4

5 SPATIAL BIG DATA has been with us for ages in various forms…but pretty invisible!! 5

6 6  Ancient Egypt  River nile  Engineers used to try data analysis to predict crop yields  LOGS & LEVEL  6695 Km long

7 Basic Intro Concepts Perceptions Challenges …the 15 min route to THANK YOU slide 7

8 An English professor wrote the words : “A Woman without her man is nothing” On the chalk board and asked his students to punctuate it correctly…. “A Woman,without her man,is nothing”. “A Woman: Without her, man is nothing” 8

9 DEFINING BIG SPATIAL DATA 9 How we understand it ?

10 Spatial data sets exceeding capacity of current computing systems…… ….to manage, process or analyze the data with reasonable effort due to Volume, Velocity, Variety and Veracity DEFINING BIG SPATIAL DATA BIG SPATIAL DATA 10

11 11 VolumeVelocityVARIETY Veracity

12 DEFINING BIG SPATIAL DATA BIG SPATIAL DATA Finding actionable info in Massive volumes of both structured and unstructured geo data that is so large and complex that it’s difficult to process with traditional database and software techniques…… Volume Velocity VARIETY VERACITY Data at rest Data in Motion Data in Many forms Data in Doubt 12

13 90% of data in the world was created in the last 2 years 3 EB of data is created every day U.S. drone aircraft sent back 35 years worth of video footage in 2012 Gigabyte (GB) - 1,024MB Terabyte (TB) - 1,024GB Petabyte (PB) - 1,024TB Exabyte (EB) - 1,024PB

14 * Estimated revenue FY 2013 growth of geospatial data is outpacing both software and services and is set to become a major contributor to the overall growth of the industry 14

15 The bad things in life open your eyes to the good things you weren’t paying attention to before SECURITY 100% security is a myth No one has said this!!! But it remains a fact 15 Increasing attack surface

16 The technolog y is ready…. But are we ready ? 16

17 17 DISASTER RELIEF FINANCIAL FRAUD DETECTION CALL CENTER REQUESTS DISEASE SURVEILLANCE INSURANCE RETAIL TELECOMMUNICATIONS UTILITIES ECO-ROUTING

18 The other of the side story 18

19 Security challenges before we adopt spatial Big data 19

20 Distributed programming frameworks Ek 20

21 Distributed programming frameworks Input file Map Intermediate Combining Shuffle Output File Local Reduce Reduce Mapper performs computation & outputs a key/value pairs 21 Reducer combines the values belonging to each distinct key and outputs the result Utilise parallelism in computation & storage to process massive amounts of data

22 MAP REDUCE FRAMEWORK  Splits the input data-set into independent chunks which are processed  in a completely parallel manner  Aggregate results from map phase  performs a summary operation  Schedules and re-runs tasks  Splits the input  Moves map outputs to reduce inputs  Receive the results Distributed programming frameworks 22

23 So challenge is not storage but it is I/O speed One Machine 4 i/o Channels Each channel : 100 MB/s 10 Machine’s 4 i/o Channels Each channel : 100 MB/s Read 1 TB 45 Min 4.5 Min

24 Untrusted Mappers Securing the data in the presence of an untrusted mapper Distributed programming frameworks 24

25 NO SQL ISSUES TWO 25

26 26 First off : the name NoSQL is not “NEVER SQL” NoSQL is not “No To SQL “

27 27 NoSQL Is simply Not Only SQL!!!!!

28 MongoDB Redis 28 NoSQL DB are still evolving with respect to security infrastructure

29 Data storage & transaction logs 29

30 STORAGE TIERS - Multi-tiered storage media -Necessitated by scalability, availability & the growth ie exponential -Different categories of data -Different types of storage Data storage & transaction logs 30

31 Lower tier means reduced security, loose access controls Keeping track of data location Data storage & transaction logs 31

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33 How can we trust data ? Validating data when source of input data is not reliable? Filtering malicious BYOD Input validation/filtering 33

34 REAL TIME MONITORING 34

35 Humongous number of alerts!!!! False positives Filtering malicious BYOD REAL TIME MONITORING 35

36 Secure communication 36

37 End to end security ? Data encryption : attribute based encryption!!!to be made richer Secure communication 37

38 Granular audits 38

39 New attacks will keep happening…and to find out we need detailed audit logs Missed true positives Granular audits 39

40 PRIVACY ISSUES 40

41 EG : How a retailer was able to identify that a teenager was pregnant before her father knew 41 PRIVACY ISSUES In the world of big data,privacy invasion is a business model

42 And... We Also Have cloud with us? 42

43 At 1.4% in Cloud was a very small percentage of the total IT spend 43

44 Pace of Big Spatial Data adoption has been Sluggish 44

45 45 There is unlikely to be a day soon in near future when we have a “FIND TERRORIST” BUTTON

46 46 We have mostly been reactive till date…..

47 47 USE KERBEROS FOR NODE AUTHENTICATION – (BUT WE KNOW IT’S A PAIN TO SET UP) STRINGENT POLICIES STANDARD TO INTRA COUNTRY LAWS EXHAUSTIVE LOGS SECURE COMMUNICATION STRINGENT POLICIES

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49 This presentation reflected the personal views and opinions in my individual capacity only. It does not represent the views and opinions of my organization or anyone else, and is not sponsored or endorsed by them in any way. This is an individual presentation. DISCLAIMER

50 Spatial big data & Security *********** ********************* *********** PPT ******************************

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52 Thursday, June 13, 11:53:14 PM 52 actuallogical

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56 Gigabyte (GB) - 1,024MB Terabyte (TB) - 1,024GB Petabyte (PB) - 1,024TB Exabyte (EB) - 1,024PB 90% of data in the world was created in the last 2 years 2.5 EB are created every day An airline jet collects 10 tb of sensor data for every 30 min of flying time. Volume of geospatial data has grown exponentially with data production crossing the storage capacity in

57 4/30/201557


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