Reference no: EM132371554 , Length: word count:3000

Security and Privacy Issues in Analytics Assignment - Technology Assessment Report

Learning outcomes - In this assignment, you will be focusing on the following unit learning outcome (ULO) -

• Apply and justify the use of a range of appropriate technological and organisational approaches to mitigate security risks associated with data analytics.
• Apply and justify the use of a range of appropriate technological and organisational approaches to mitigate privacy risks associated with data analytics.
• Evaluate vulnerabilities in organization and personal data and justify appropriate strategies to attack and defend.

Brief description of the task -

You are required to write a 2500-3000 word technology assessment report for the Dumnonia Corporation.

The report relates to the implementation of k-anonymity as a model for protecting privacy of sensitive data for Dumnonia Corporation.

Instructions -

For this assessment, you have been interviewing the following Dumnonia staff, interview notes of which shall be made available through the FutureLearn/CloudDeakin platform:

• Caradoc - CEO - Interview notes based upon the use of k-anonymity by Dumnonia from an organisational perspective;
• Guinevere - CIO - Interview notes based upon the use of k-anonymity and implementation of k-anonymity for Dumnonia. This interview will be a technical perspective;
• Constantine - CSO - Interview notes based upon the implementation of k-anonymity for Dumnonia. This interview will be a technical perspective;

The k-anonymity technique allows for clustering of data based on the publicly known handle of each data sample of a given dataset, whilst keeping the sensitive part of the data sample a secret.

Consequently, the clustering of data is achieved, such that each cluster contains k items and the sensitive data associated with each item is kept a secret. k-anonymity mitigates the requirement for encrypting data, which could otherwise prove to be a costly operation.

For this assignment, prepare a report that addresses the following points:

1. Organisational drivers for Dumnonia that require anonymization techniques to solve their problems.

2. Description of technology solutions that can be employed to implement k-anonymity within Dumnonia Corporation.

3. Development of an implementation guide for Dumnonia that can be followed in order to apply k-anonymity to the sensitive data of Dumnonia.

4. Additionally, you should compare two different implementations of k-anonymity (either by running experiments on the adult and INFORMS datasets) or by reporting a detailed study of two types of k-anonymity techniques.

For implementation: either download two implementations of k-anonymity code, or write code of your own. You can download the adult and INFORMS datasets, and produce the Normalised Certainty Penalty (NCP) percentage for each dataset for K=10, and also record the execution times.

For comparison of two approaches: Some examples you could compare are Mondrian vs Clustering Based approaches, but any published algorithm would be acceptable.

5. Compare the two methods to suggest which approach Dumnonia Corporation may wish to adopt. If you implemented your own method, you do not need to compare and contrast approaches, simply report the NCP percentage and the execution code of your code in the report. Otherwise, report on the comparison of the two methods in terms of which one best suits the problem faced by Dumnonia Corporation.

Report structure - The report should be structured in the following way:

• Executive Summary of your report findings (250 words)
• Organisational Drivers for Dumnonia (250 words)
• Discussion of the organisational drivers for Dumnonia relating to the implementation of k-anonymity (400 words)
• Technology Solution Assessment (500 words)
• Detailed assessment of the technologies that Dumnonia have selected on how to implement k-anonymity for protecting privacy of their sensitive data (500 words)
• k-anonymity Implementation Guide (500 words)
• Comparison of two publically available implementations or k-anonymity techniques, or writing and discussion of your own code. (500 words).

Attachment:- Security and Privacy Issues in Analytics Assignment Files.rar