SIT719 Security and Privacy Issues in Analytics Distinction/High Distinction Task 9.1: Location-based Privacy Protection Overview Trajectory data is powerful to many crowdsourcing tasks. For example, Uber and other services use the drivers’ geolocation to match the client’s requests. However, there are serious concerns about the privacy of publishing the geolocation data. In this Distinction/Higher Distinction Task, you will experiment with machine learning classification algorithms. Please see more details in the Task description. Before attempting this task, please make sure you are already up to date with all previous Credit and Pass tasks. Task Description Instructions: Suppose that you are hired by a large company that uses the user’s geolocation data as references for allocating crowdsourcing tasks. The company has developed good algorithms for allocating tasks based on accurate geolocation data. One of the new business requirements is that each client can visualize a few nearby crowdsourcing workers before the client finalizes the order. Displaying the geolocation on Google Maps and alike services is doable. However, the accurate geolocation data is sensitive and cannot be directly disclosed to the clients. Therefore, the boss has requested you to develop a demo system to protect the privacy of the geolocation data. Since this is a demo system, the famous dataset named “Gowalla” is provided to simulate the crowdsourcing workers, which is available at https://allaplusessays.com/order. The Gowalla dataset consists of multiple users’ check-in data with timestamps in five columns, some sample data look like this: [user] [check-in time] [latitude] [longitude] [location id] 196514 2010-07-24T13:45:06Z 53.3648119 -2.2723465833 145064 1965142010-07-24T13:44:58Z53.360511233-2.27636901712759911965142010-07-24T13:44:46Z53.3653895945-2.27540870463764971965142010-07-24T13:44:38Z53.3663709833-2.2700764333985031965142010-07-24T13:44:26Z53.3674087524-2.278381347710434311965142010-07-24T13:44:08Z53.3675663377-2.2786317638817341965142010-07-24T13:43:18Z53.3679640626-2.2792943689207763 196514 2010-07-24T13:41:10Z 53.364905 -2.270824 1042822 You will need to download the dataset, familiarize with it before performing the following actions: Find three privacy protection methods to protect location-based data from at least three published papers, including the papers published on arxiv.org.Write a short literature review (approximately 500 words) to compare the identified methods in at least three aspects that are relevant to data privacy and utility.Identify meaningful performance metrics based on your critical literature review and comparison before proposing how to measure these metrics.Implement or apply the existing implementations of privacy protection methods on the Gowalla dataset.Report the performance metrics that are identified in Step 3.Demonstrate the proposed solutions with a few case studies using Google maps. A simple illustration of your demo may look like the following, where Pi are the crowdsourcing workers, ai are the clients: Once you have completed the above steps of the project, you need to deliver the outcome. In real-world, results are typically delivered as a product/tool/web-app or through a presentation or by submitting the report. However, in our unit, we will consider a report and a demo only. Here, you need to write a report (at least 2,000 words including the above-mentioned literature review) based on the outcome and results you obtained by performing the above steps. The report will describe the literature review, the algorithms used, their working principle, key parameters, and the results. Results should consider all the key performance measures and comparative results in the form of tables, graphs, etc. The demo should be a 5-minutes long pre- recorded presentation. Submit the PDF report and the demo PPT through OnTrack. You also need to submit the code separately (within the “Code for task 9.1” folder) under the assignment tab of the CloudDeakin Python script(s) during submission. Marking Rubric: CriteriaUnsatisfactory – BeginningDevelopingAccomplishedExemplaryTotalReport Focus: Purpose/ Position Stateme nt0-7 points8-11 points12-15 points16-20 points/20Fails to clearly relate the report topic or is not clearly defined and/or the report lacks focus throughout.The report is too broad in scope (outside of the title topic) and/or the report is somewhat unclear and needs to be developed further. Focal point is not consistently maintained throughout the report.The report provides adequate direction with some degree of interest for the reader. The report states the position, and maintains the focal point of the analysis for the most part.The report provides direction for the discussion part of the analysis that is engaging and thought provoking, The report clearly and concisely states the position, and consistently maintain the focal point.Compar ative analysis and Discussi on0-15 points16-20 points21-24 points25-30 points/30Demonstrates a lack of understanding and inadequate knowledge of the topic. Analysis is very superficial andDemonstrates general understanding of python scripting. Analysis is good and has addressed all criteria. ComparativeDemonstrates good level of understanding of python scripting. Algorithms are fine-tuned and comprise goodDemonstrates superior level of understanding of python scripting and algorithms. Algorithms are fine-tuned with some contains flaws. Theanalysis is presented.selection of algorithms.novelty or hybridization or report is also not clear.Sufficient discussion isComparative results areadvanced and/or recent also presented.presented using standardalgorithm. Comparative performance measures.results are presented using performance measures in a way that it provides very clear and meaningful insights of the output. Demonst ration0-6 points7-11 points12-15 points16-20 points/20Demonstration lacksDemonstration includes aDemo is working andProfessionally conducted coherent ideas and failsworking system,clearly explained,demo, free from errors, to demonstrate ahowever, the benefits ofhowever, there might beexcellent talk with deep working system.privacy protection are notoccasional mistakes orknowledge on privacy clearly presented.difficult points toprotection for location understand.data. Writing Quality & Adheren ce to Format Guidelin es0-10 points11-17 points18-21 points22-30 points/30Report shows a below average/poor writing style lacking in elements of appropriate standard English. Frequent errors in spelling, grammar, punctuation, spelling, usage, and/or formatting.Report shows an average and/or casual writing style using standard English. Some errors in spelling, grammar, punctuation, usage, and/or formatting.Report shows above average writing style (can be considered good) and clarity in writing using standard English. Minor errors in grammar, punctuation, spelling,Article is well written and clear and standard English characterized by elements of a strong writing style. Basically free from grammar, punctuation, spelling, usage, and/or formatting.usage, or formatting Author has demonstratederrors. the use of scientificAuthor has demonstrated language and results areadvanced use of well explained.scientific language and results are well explained with insights. Rubric adopted from: Denise Kreiger, Instructional Design and Technology Services, SC&I, Rutgers University, 4/2014
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