Big Data
Concept

data are growing and changing in a rapid way

data come in different and multiple formats

huge amount of data is generated every second

the defined purpose of Big Data mining

processed data comply to some specifications

the purpose is fulfilled

pertinent information can be extracted for the
benefit of many sectors

it is difficult to organize and analyse Big Data
because of evolving data relationships

collected and stored Big Data can be permanent
if well managed

Big Data Nature

Adding security layers may slow system performances
and affect dynamic analysis

It is difficult to handle data classification and management
of large digital disparate sources

Sharing data over many networks increase security risks

The Need to Share
Information

Multiple connections with different levels
of securities

Data sharing associated with advanced
analytics techniques

Discovering confidential information

Illegal access to network's traffics

Correlation attacks, arbitrary identification,
intended identification attacks, etc

Multiple Security
Requirements

To handle information security while managing
massive and rapid data streams

Security tools should be flexible and easily scalable

There is a need to find a balance between multiple
security requirements, privacy obligations, system
performance and rapid dynamic analysis

Inadequate Traditional
Solutions

e.g.: types of data encryption

slow the performance

are time-consuming

are not efficient

New Security Tools
Lack of Maturity

Data Anonymization

Should be achieved without affecting system
performance or data quality

Traditional anonymization techniques are based
on several iterations and time consuming computations

may affect data consistency

may slow down system performance

It is difficult to process and analyse anonymized
Big Data

Compatibility with Big
Data Technologies

some security techniques are incompatible
with commonly used Big Data technologies
(e.g.: MapReduce)

It is mandatory to verify their compatibility
with organization Big Data requirements and
existing infrastructure components.
(Zhao et al., 2014)

Information Reliability
and Quality

It is important to verify Big Data sources
authenticity and integrity before analysing
data

It is difficult to assess the authenticity and
integrity of all various data sources

Data have to be filtered, organized and
contextualized before performing any analysis

Compliance to Security
Laws Regulations and Policies

Deal with multiple laws and regulations
(Tankard, 2012)

Big Data analytics may be in conflict
with some privacy principles.

Need of Big Data Experts

need for advanced security analysis experts
(Constantine, 2014)

Big Data Security on Social Networks

Can prevent terrorist and security
attacks and assess citizens' satisfaction
regarding public services

Detect rapidly abnormal patterns and
ensure a real-time monitoring of
alarming events

a real-time monitoring to detect vulnerabilities,
security threats and abnormal behaviours

a granular role-based access control

a robust protection of confidential information

a generation of security performance indicators

Information Security

Security Monitoring

Data Security

ensure Big Data management

ensure system integrity

ensure cyberspace security

Security Foundations
for Big Data Projects

Risk Analysis Related to
Multiple Technologies

Choosing Adequate
Security Solutions

Dynamic analysis

To detect timely
security incidents

To identity abnormal
customer's behaviours

To monitor security threats

To discover known and
new cyber-attack patterns

Anonymization of Confidential
or Personal Data

Models for data
anonymization

Sub-tree

Top-Down Specialization
(TDS)

Bottom-Up Generalization
(BUG)

Hybrid approach
(TDS + BUG)

t-closeness

m-invariance

k-anonymity

l-diversity

Data Cryptography

Homomorphic Cryptography

Cloud Background Hierarchical
Key Exchange (CBHKE)

Centralized Security
Management

Data Confidentiality and
Data Access Monitoring

Security Surveillance
and Monitoring

Data Loss Prevention
(DLP)

Security Information and Event
Management (SIEM)

dynamic analysis of security
events