Big Data is getting bigger and bigger but at the same time before adopting it seriously and exploiting it we should also take care of the security shortcomings it comes up with....from a forensics and security point of view....we need to understand the vulnerabilities they come up with before blindly adopting them!!!!

1. Geo Intelligence India
13-14 Jun 2013
New Delhi

2. Do lafzon ki hai DATA ki kahani...............
Ek hai ZERO....duja hai ONE.....
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3. Big Spatial Data
Security
WELCOME
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4. BIG SPATIAL DATA has been with us for ages in
various forms…but pretty invisible!!
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5. 5
Ancient Egypt
River nile
Engineers used
to try data
analysis to
predict crop
yields
6695 Km long

6. Basic Intro
Concepts
Perceptions
Challenges
…the 15 min route to THANK YOU slide
6

7. 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”
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8. A greater scope of Geo Int info
New kinds of Geo data and analysis
Real time Geo information
Data influx from new technologies
Non traditional forms of Geo data
Large volumes of Geo data
The latest buzzword
Social media data
0 2 4 6 8 10 12 14 16 18 20
Series 1
DEFINING BIG SPATIAL DATA
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How we understand
it ?

9. 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
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10. 10
DATA is Exploding in
Volume Velocity VARIETY
While decreasing in
Veracity

11. 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
11

12. 90% of data in
the world was
created in the
last 2 years
2.5 EB of
data is
created
every day
U.S. drone aircraft
sent back 24 years
worth of video
footage in 2009
Gigabyte (GB) - 1,024MB
Terabyte (TB) - 1,024GB
Petabyte (PB) - 1,024TB
Exabyte (EB) - 1,024PB

13. * 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
13

14. 100% security is a myth
No one has said this!!!
But it remains a fact
14
Increasing attack
surface

15. The technology is
ready….
But are we ready
?
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16. 16
16
DISASTER RELIEF
FINANCIAL
FRAUD DETECTION
CALL CENTER REQUESTS
DISEASE SURVEILLANCE
INSURANCE
RETAIL
TELECOMMUNICATIONS
UTILITIES
ECO-ROUTING

17. The other
of the
side
story
17

18. Security challenges before we adopt
Big spatial data
18

19. Distributed programming frameworks
Ek
19

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

21. 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
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22. 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

23. Untrusted Mappers
Securing the data in the
presence of an untrusted
mapper
Distributed programming frameworks
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24. NO SQL ISSUES
TWO
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25. 25
First off : the name
NoSQL is not “NEVER SQL”
NoSQL is not “No To SQL “

26. 26
NoSQL
Is simply
Not Only SQL!!!!!

27. MongoDB
Redis
27
NoSQL DB are still
evolving with
respect to security
infrastructure

28. Data storage & transaction logs
28

29. STORAGE TIERS
- Multi-tiered storage media
- Necessitated by scalable size
- Different categories of data
- Different types of storage
Data storage & transaction logs
29

30. Lower tier means reduced
security, loose access
controls
Keeping track of data
location
Data storage & transaction logs
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31. INPUT
VALIDATION/FILTERING
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32. How can we trust data ?
Validating data when source
of input data is not reliable?
Filtering malicious data @
BYOD
Input validation/filtering
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33. REAL TIME
MONITORING
33

34. Humongous number of
alerts!!!!
False positives
Filtering malicious data @
BYOD
REAL TIME MONITORING
34

35. Secure communication
35

36. End to end security ?
Data encryption : attribute based encryption!!!to be
made richer
Secure communication
36

37. Granular audits
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38. New attacks will keep
happening…and to find
out we need detailed
audit logs
Missed true positives
Granular audits
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39. PRIVACY ISSUES
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40. EG : How a retailer was
able to identify that a
teenager was pregnant
before her father knew
40
PRIVACY ISSUES
In the world of big data,privacy invasion is a business model

41. And...
We
Also Have cloud with us?
41

42. At 1.4% in 2011-12
Cloud was a very small
percentage of the total IT spend
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43. Pace of Big Spatial Data adoption has been
Sluggish
43

44. 44
There is unlikely to be
a day soon in near
future when we have a
“FIND
TERRORIST”
BUTTON

45. 45
We have mostly
been reactive till
date…..

46. 46
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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