5.2.13

Analysing Personal Information

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Analysing Personal Information

Companies increasingly value personal data for its ability to reveal insights about behaviour. The practice raises ethical concerns regarding privacy and transparency.

Data mining

Data mining

  • In recent years, companies have recognised the immense value of personal data.
  • Companies use large datasets such as big data to analyse and make inferences about individual behaviours, preferences, and trends.
  • Using large datasets to make inferences about individuals is known as data mining.
    • Data mining is employed for various purposes, including targeted advertising or personalised political campaigns.
Ethical concerns

Ethical concerns

  • Analysing personal information can enhance user experiences and business strategies.
  • Analysing personal information also raises significant ethical concerns.
    • Especially when data is used in ways that may disadvantage individuals.
    • For example, when data is used to influence insurance decisions based on health risk predictions.
Privacy & transparency

Privacy & transparency

  • The collection and analysis of personal data have sparked debates about privacy.
    • The debates highlighted the need for transparency from companies.
  • Businesses are legally required to disclose what data they collect.
    • Nonetheless, users often remain unaware of the extent and purpose of a business's data gathering.
GDPR

GDPR

  • Growing pressure on companies to be more transparent has led to regulatory measures.
  • The General Data Protection Regulation (GDPR) was enforced in 2018.
    • GDPR aims to protect user rights and ensure clearer communication about data usage.
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1

Components of a Computer

1.1

Structure & Function of the Processor

1.1.1Von Neumann Architecture1.1.2Registers1.1.3Buses1.1.4Fetch, Decode, Execute Cycle1.1.5Factors Affecting CPU Performance1.1.6Pipelining1.1.7CPU Architecture
1.2

Types of Processors

1.2.1CISC & RISC1.2.2GPUs & Their Uses1.2.3Multicore & Parallel Systems
1.3

Input, Output & Storage

1.3.1Elements of Computer Systems1.3.2Types of Computer Systems1.3.3How Magnetic Storage Works1.3.4Properties of Magnetic Storage1.3.5Examples of Magnetic Storage1.3.6How Optical Storage Works1.3.7Properties of Optical Storage1.3.8Examples of Optical Storage1.3.9Types of Optical Disc1.3.10Random Access Memory1.3.11Read Only Memory1.3.12Uses of Flash Memory1.3.13Properties of Flash Memory1.3.14What to do When We Run Out of Memory1.3.15How Virtual Memory Works
2

Software & Software Development

2.1

Systems Software

2.1.1Operating Systems2.1.2Managing Memory2.1.3Paging2.1.4Segmentation2.1.5Device Management & Drivers2.1.6Interrupts2.1.7Interrupt Service Routine (ISR)2.1.8Scheduling2.1.9Types of Scheduling2.1.10Types of Operating System2.1.11BIOS2.1.12Virtual Machines
2.2

Applications Generation

2.2.1Applications Software2.2.2Utilities2.2.3Encryption Software2.2.4Defragmentation Software2.2.5Data Compression Software2.2.6Backup Software2.2.7Open Source Software2.2.8Proprietary Software2.2.9Licensing Issues2.2.10Compilers2.2.11Interpreters2.2.12Assemblers2.2.13Compiling a Program2.2.14Lexical Analysis2.2.15Compilation Stages2.2.16Linkers, Loaders & Libraries
2.3

Software Development

2.3.1Algorithmic Thinking2.3.2Waterfall Lifecycle2.3.3Waterfall Lifecycle - Strengths & Weaknesses2.3.4Agile Methodology2.3.5Agile Methodology - Strengths & Weaknesses2.3.6Extreme Programming2.3.7Extreme Programming - Strengths & Weaknesses2.3.8Spiral Methodology2.3.9Spiral Methodology - Strengths & Weaknesses2.3.10Rapid Application Development2.3.11RAD - Strengths & Weaknesse
2.4

Types of Programming Language

2.4.1High Level Programming Languages2.4.2Low Level Programming Languages2.4.3Programming Paradigms2.4.4Procedural Programming Languages2.4.5Assembly2.4.6Machine Code2.4.7Modes of Addressing Memory2.4.8Object-Oriented Languages2.4.9Classes2.4.10Objects2.4.11Encapsulation2.4.12Polymorphism
3

Exchanging Data

3.1

Compression, Encryption & Hashing

3.1.1Compression3.1.2Run Length Encoding3.1.3Dictionary Coding3.1.4Encryption3.1.5Symmetric Encyrption3.1.6Hashing
3.2

Databases

3.2.1Records3.2.2Databases3.2.3Relational Databases3.2.4Entity Relationship Modelling3.2.5Managing Databases3.2.6Normalisation3.2.7SQL3.2.8Principles of Data Storage3.2.9Atomicity, Consistency, Isolation & Durability
3.3

Networks

3.3.1The Benefits of Networks3.3.2Network Performance3.3.3Types of Networks3.3.4Network Protocols3.3.5Transmission Protocols3.3.6What is the Internet?3.3.7Uniform Resource Locators3.3.8Domain Name Service3.3.9Web Hosting3.3.10Layering Concepts3.3.11TCP &. OSI Models3.3.12The Advantages of Layering3.3.13What's in a Packet?3.3.14How do Packets get Routed?3.3.15Did my Data Arrive Safely?3.3.16Network Hardware3.3.17Transmission Media3.3.18Firewalls3.3.19Proxies3.3.20Client-Server Model3.3.21Advantages of the Client Server Model3.3.22Disadvantages of the Client Server Model3.3.23Peer-to-Peer Model3.3.24Advantages of the Peer-to-Peer Model3.3.25Disadvantages of the Peer-to-Peer Model
3.4

Web Technologies

3.4.1HTML3.4.2CSS3.4.3JavaScript3.4.4Search Engine Indexing3.4.5PageRank Algorithm3.4.6Server & Client-Side Processing
4

Data Types, Data Structures & Algorithms

4.1

Data Types

4.1.1Data Types4.1.2Casting4.1.3Arrays4.1.42D Arrays4.1.5Strings4.1.6Binary4.1.7Sign & Magnitude4.1.8Binary Addition4.1.9Binary Shifts4.1.10Hexadecimal4.1.11Using Hexadecimal4.1.12Converting Binary & Hexadecimal4.1.13Converting Denary & Hexadecimal4.1.14Floating Points in Binary4.1.15Normalisation of Floating Points4.1.16Floating Point Addition4.1.17Floating Point Subtraction4.1.18Bitwise Manipulation - Shifts4.1.19Bitwise Manipulation - Masks4.1.20Character Sets4.1.21ASCII4.1.22Unicode
4.2

Data Structures

4.2.13D Arrays4.2.2Linked List4.2.3Queues4.2.4Graphs4.2.5Hash Tables4.2.6Stacks4.2.7Trees4.2.8Binary Search Trees
4.3

Boolean Algebra

4.3.1Binary Systems4.3.2Logic Circuits4.3.3Boolean Operators4.3.4Evaluating Expressions4.3.5Boolean Logic4.3.6Karnaugh Maps4.3.7Boolean Identities4.3.8Boolean Rules4.3.9Flip Flops4.3.10Adder Circuits
5

Legal, Moral, Cultural & Ethical Issues

5.1

Computing Related Legislation

5.1.1Data Protection Act (1998)5.1.2Computer Misuse Act (1990)5.1.3Copyright Designs & Patents Act (1988)5.1.4Creative Commons Licensing5.1.5Regulation of Investigatory Powers Act (2000)
5.2

Moral & Ethical Issues

5.2.1Online Activity Tracking5.2.2Censorship5.2.3Positive Cultural Impacts5.2.4Negative Cultural Impacts5.2.5E-Waste5.2.6Energy Consumption5.2.7Positive Environmental Impact5.2.8Layout, Colour Paradigms & Character Sets5.2.9Computers in the Workplace5.2.10Automated Decision-Making5.2.11Artificial Intelligence5.2.12Monitoring Behaviour5.2.13Analysing Personal Information5.2.14Piracy & Offensive Communication
6

Elements of Computational Thinking

6.1

Thinking Abstractly

6.1.1Abstraction
6.2

Thinking Ahead

6.2.1Inputs & Outputs
6.3

Thinking Procedurally

6.3.1Decomposition
6.4

Thinking Logically

6.4.1Pseudocode6.4.2Flow Diagrams
6.5

Thinking Concurrently

6.5.1Concurrent Processing
7

Problem Solving & Programming

7.1

Programming Techniques

7.1.1Variables & Constants7.1.2Comments7.1.3Inputs & Outputs7.1.4Subroutines7.1.5Scope
7.2

Programming Construction

7.2.1Integrated Development Environments7.2.2The Purpose of Testing7.2.3Types of Testing7.2.4Test Plans
8

Algorithms

8.1

Algorithms

8.1.1Interpreting Algorithms8.1.2Correcting Algorithms8.1.3Completing Algorithms8.1.4Sort Algorithms8.1.5Bubble Sort8.1.6Insertion Sort8.1.7Merge Sort8.1.8Search Algorithms8.1.9Linear Search8.1.10Binary Search8.1.11Quick Sort Algorithm8.1.12A* Algorithm

Practice questions on Analysing Personal Information

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    When was the GDPR enforced?Multiple choice
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Monitoring Behaviour

Piracy & Offensive Communication