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SIT719 :security and privacy issues in analytics

Pages9
SIT719 Security and Privacy Is ...

Answer: Introduction With the exponential growth   of use of big data analytics the privacy and ethical issues related to the use of the datasets and insights from the data are also rais ...

Course

SIT719

Type

sample

University

Deakin University

Pages10
SIT719 Security and Privacy Is ...

Answer: Introduction The analytical datasets should be protected for ensuring that the network traffic and business applications are secured (Dwork and Roth 2014). The following report outlines a br ...

Course

SIT719

Type

sample

University

Deakin University

Pages11
SIT719 Security and Privacy Is ...

Answer: Introduction: The risks of security and privacy issues in analytics originates from storing, managing and analyzing various information collected from various available and possible sourc ...

Course

SIT719

Type

sample

University

Deakin University

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More SIT719 security and privacy issues in analytics: Questions & Answers

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SIT719 Real World anlytics

1. A brewery produces beer and ale. Beer sells for $ 5 per barrel, and ale for $ 2 per barrel. The production of a barrel of beer requires 5 pounds of corn and 2 pounds of hops. The production of a barrel of ale requires 2 pounds of corn and 1 pound of hops. 60 pounds of corn and 25 pounds of hops are available. a) Explain why a Linear Programming (LP) model would be suitable for this case study. ...

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SIT719 Analytics for Security and Privacy

Task In this section, refer to the tasks you have completed. These will be attached by OnTrack after this summary. Do not try to demonstrate the outcomes here, this is just a summary. Think of this like a cover letter to a job application. The unit learning outcomes are the job’s selection criteria. Your tasks provide the evidence of how you have met these criteria. Reflections: The mos ...

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SIT719 Security and Privacy Issues in Analytics

Questions: Question 1: Organisational drivers for dumnonia that require anonymization techniques to solve their problems. Question 2: Description of technology solutions that ca be employed to implement k-anonymity within dumnonia corporation. Question 3: Development of an implementation guide for dumnonia that can be followed in order to apply k-anonymity to the sensitive data of dumnonia. ...

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Referencing Related to SIT719 security and privacy issues in analytics

HEITMANN, B., HERMSEN, F. AND DECKER, S., 2017

K-RDF-Neighbourhood Anonymity: Combining Structural And Attribute-based Anonymisation For Linked Data.

In-text: (In [email protected] ISWC.)

Your Bibliography: Heitmann, B., Hermsen, F. and Decker, S., 2017. k-RDF-Neighbourhood Anonymity: Combining Structural and Attribute-based Anonymisation for Linked Data. In [email protected] ISWC.

KIM, J.S. AND LI, K.J., 2016.

Location K-anonymity In Indoor Spaces.

In-text: (Geoinformatica, 20(3), pp.415-451.)

Your Bibliography: Kim, J.S. and Li, K.J., 2016. Location K-anonymity in indoor spaces. Geoinformatica, 20(3), pp.415-451.

LIU, X., XIE, Q. AND WANG, L., 2017.

Personalized Extended (?, K)?anonymity Model For Privacy?preserving Data Publishing.

In-text: (Concurrency and Computation: Practice and Experience, 29(6), p.e3886.)

Your Bibliography: Liu, X., Xie, Q. and Wang, L., 2017. Personalized extended (?, k)?anonymity model for privacy?preserving data publishing. Concurrency and Computation: Practice and Experience, 29(6), p.e3886.

NIU, B., LI, Q., ZHU, X., CAO, G. AND LI, H., 2014, APRIL.

Achieving K-anonymity In Privacy-aware Location-based Services.

In-text: (In INFOCOM, 2014 Proceedings IEEE (pp. 754-762). IEEE.)

Your Bibliography: Niu, B., Li, Q., Zhu, X., Cao, G. and Li, H., 2014, April. Achieving k-anonymity in privacy-aware location-based services. In INFOCOM, 2014 Proceedings IEEE (pp. 754-762). IEEE.

OTGONBAYAR, A., PERVEZ, Z., DAHAL, K. AND EAGER, S., 2018.

K-VARP: K-anonymity For Varied Data Streams Via Partitioning.

In-text: ( Information Sciences, 467, pp.238-255.)

Your Bibliography: Otgonbayar, A., Pervez, Z., Dahal, K. and Eager, S., 2018. K-VARP: K-anonymity for varied data streams via partitioning. Information Sciences, 467, pp.238-255.

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