Big_data_security_and_privacy_for_CSE.pdf

this is an ppt for big data

1. BIG DATA IN SECURITY AND PRIVACY
NAME : K.KEVIN GLADSON
ROLL NO. : 112210
CLASS : CSE ‘B’

2. INTRODUCTION
•Definition
•Large volumes of data generated at high velocity and variety.
•Includes structured, semi-structured, and unstructured data.
•Importance
•Key Driver of Innovation: Enables better decision-making, product improvements, and enhanced customer experiences
across sectors like healthcare, retail, and finance.
Growth
•Increasing Data Generation: Exponentially growing due to:
•Social Media: Massive user-generated content daily.
•Internet of Things (IoT): Continuous data from connected devices.
•Digital Transactions: Data from e-commerce and online banking activities.

3. BIG DATA SECURITY
Role of Big Data in Security
1.Enhancing Cybersecurity
1.Leverage large datasets to identify and mitigate threats.
2.Analyze vast amounts of data for real-time threat intelligence.
2.Threat Detection
1.Use analytics to detect abnormal patterns.
2.Identify potential breaches by monitoring user behavior and network activity.
3.Prevention
1.Proactively address vulnerabilities.
2.Use insights from data analysis to strengthen security measures and prevent attacks.

4. APPLICATIONS OF BIG DATA IN SECURITY
Examples of Big Data Applications
1.Fraud Detection
1.Real-time analysis of transactions.
2.Identifying and preventing fraudulent activities.
2.Intrusion Detection Systems (IDS)
1.Monitoring network traffic.
2.Detecting suspicious behavior and potential intrusions.
3.User Behavior Analytics (UBA)
1.Understanding user actions.
2.Detecting anomalies to identify potential security threats.

5. BIG DATA IN PRIVACY
Privacy Concerns with Big Data
1.Data Collection
1.Extensive gathering from various sources.
2.Raises significant privacy issues.
2.Surveillance
1.Potential misuse of data.
2.Monitoring individuals without consent.
3.Personal Data Protection
1.Challenges in safeguarding sensitive information.
2.Ensuring data security and privacy.

6. CHALLENGES IN PRIVACY
Key Privacy Challenges
1.Data Breaches
1.Unauthorized access.
2.Leads to significant data leaks.
2.Unauthorized Data Access
1.Ensuring access control.
2.Only authorized personnel can access sensitive data.
3.Anonymity and De-anonymization
1.Risks of re-identifying anonymized data.
2.Ensuring true data anonymity.

7. LEGAL AND ETHICAL CONSIDERATIONS
Regulatory Frameworks
1.General Data Protection Regulation (GDPR)
1.European Union regulation on data protection.
2.Sets standards for data privacy and security.
2.California Consumer Privacy Act (CCPA)
1.U.S. regulation on consumer data privacy.
2.Provides rights to consumers regarding their personal data.
3.Other Relevant Regulations
1.Overview of global data protection laws.
2.Ensures compliance with various international standards.

8. TECHNOLOGIES FOR PRIVACY
PRESERVATION
Privacy-Preserving Technologies
1.Encryption
1.Protecting data through cryptographic techniques.
2.Ensures data is unreadable to unauthorized users.
2.Data Anonymization
1.Removing personally identifiable information from datasets.
2.Helps protect individual privacy.
3.Differential Privacy
1.Ensuring individual data privacy while analyzing large datasets.
2.Adds noise to data to prevent re-identification.

9. CASE STUDIES
Real-World Examples
1.Case Study 1
1.Big data analytics helped in a significant cybersecurity breach.
2.Identified and mitigated threats effectively.
2.Case Study 2
1.Successful implementation of privacy-preserving technologies.
2.Enhanced data protection in a major corporation.
3.Case Study 3
1.Government use of big data for national security.
2.Addressed privacy concerns while enhancing security measures.

10. BEST PRACTICES
Strategies for Balancing Security and Privacy
1.Robust Security Protocols
1.Implementing multi-layered security measures.
2.Protects data from various types of threats.
2.Compliance with Privacy Regulations
1.Ensuring adherence to laws like GDPR and CCPA.
2.Maintains legal standards for data protection.
3.Regular Audits and Risk Assessments
1.Continuously evaluating security and privacy practices.
2.Identifies and addresses vulnerabilities.

11. FUTURE TRENDS
Emerging Trends in Big Data, Security, and Privacy
1.AI and ML in Security
1.Using artificial intelligence for predictive analytics.
2.Enhancing threat detection and response.
2.Advances in Privacy-Enhancing Technologies
1.Development of new tools and methods.
2.Protecting privacy more effectively.
3.Predictive Analytics
1.Leveraging data to anticipate threats.
2.Mitigating future security risks.

12. CONCLUSION
Summary of Key Points
1.Importance of Big Data in Security
1.Enhances cybersecurity measures.
2.Provides insights for proactive threat management.
2.Ongoing Challenges in Protecting Privacy
1.Addressing data breaches and unauthorized access.
2.Ensuring true data anonymity.
3.Need for a Balanced Approach
1.Balancing security with privacy considerations.
2.Continuous improvement in practices and technologies.