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Privacy Aware Computing, One week notes

by: Sainath Notetaker

Privacy Aware Computing, One week notes CS-7850-01

Marketplace > Wright State University > ComputerScienence > CS-7850-01 > Privacy Aware Computing One week notes
Sainath Notetaker
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About this Document

This material is having Course introduction and purpose of this course and what these course will cover.
Privacy Aware Computing
Dr. John W. Carls
Class Notes
Privacy issues




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This 5 page Class Notes was uploaded by Sainath Notetaker on Saturday January 16, 2016. The Class Notes belongs to CS-7850-01 at Wright State University taught by Dr. John W. Carls in Spring 2016. Since its upload, it has received 33 views. For similar materials see Privacy Aware Computing in ComputerScienence at Wright State University.


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Date Created: 01/16/16
Privacy Aware Computing Study Soup Note taker Course Overview: This course is taught by “Dr. John W. Carls” and there is no particular textbook will be available for this course. The course work will includes reading articles from conferences, journals and news sources.   What kinds of topics would you expect to be covered in a privacy aware  computing course?  Individual privacy o Customer data:  Customer can be collected at different ways like credit information, survey etc., that can be misused for various security issues like hacking. o Public data:  Census data, voting record: The personal data will be available in this voting record, census data which is sensitive data. By using this information they can use for wrong deals. o Health record:  Using patient’s information, we will track the patient’s health status which might be useful for insurance companies and personal medical records help them maximize profit. But, how well they are securing the information which can be sensitive can be misused. o Locations:  They are knowing our location which can also be privacy issue. Because sometimes we might wanted to be alone or we might wanted to be with our family without any disturbances. o Online activities:  Almost every work became online, it gave us time saving but “Is customer information is kept safe is the million dollar question?” For Example, everyone does online shopping in flipkart or amazon. For that we need register with all our information that includes sensitive information like SSN, Credit card details, phone number etc. If that information leak outside means, then it will be great security issue for everyone.      Organization privacy o Owning collections of personal data:  Collecting personal data from the customers is common for organizations like Walmart, Meijer in some way. They will track the customer’s purchase transactions and they will develop new business techniques which is beneficial for them. o Business secrets:  They will be several business tactics which is their base for their company that they don’t want to share anyone but they want to use it for future use which is having privacy issue.  For example, Multinational corps may want to pool data from different countries for analysis, but national laws may prevent trans­border data sharing. o Legal issues prevent data sharing: Through data mining, we are able to find new business tactics to improve business but if there is any privacy breach then there will be so many legal issues.   Differences between security and privacy • Privacy: decisions on what personal information is released and who can access it. • Security makes sure these decisions are respected  • Security is often a necessary method to implement privacy  What does this course cover? • Data anonymization and differential privacy • Data perturbation and randomized responses • Privacy preserving data mining • Privacy in social networks • Privacy preserving information retrieval • Location privacy • Legal, social and psychological aspects of privacy • Secure data outsourcing • Data anonymization and differential privacy:  Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of either encrypting or removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous. For example, in medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information. The name, address, and full post code must be removed together with any other information which, in conjunction with other   data held by or disclosed to the recipient, could identify the patient. De­anonymization is the reverse process in which anonymous data is cross­referenced with other data sources to re­ identify the anonymous data source. Differential   privacy  is   used  to   provide   means   to   maximize   the   accuracy of queries from statistical databases while minimizing the chances of identifying its records. It means that your sensitive information couldn’t be made safe by refusing to have your information in a dataset. Consider a trusted party that holds a dataset of sensitive information (e.g. medical records, voter registration information, email usage) with the goal of providing global, statistical information about the data publicly available, while preserving the privacy of the users whose information the data set contains. Such a system is called a statistical database. The notion of indistinguishability, later termed Differential Privacy, formalizes the notion of "privacy" in statistical databases. • Data perturbation and randomized responses: The original data values are replaced with synthetic values that preserve chosen  statistical properties of the data (e.g. mean, standard deviation, etc.) is data perturbation. • Privacy preserving data mining: How we are going to secure personal information during data mining which  shouldn’t get any sensitive information.  • Privacy in social networks: When we are signing up for any social network, it will ask for all our personal  information. Is it having strong privacy?  Because leaking of any personal information which is sensitive can be a security  issue. • Privacy preserving information retrieval:  For example, Walmart will be having so many transaction which is stored in the  data bases. They will do the data mining to get benefit by using privacy model. After  using some privacy techniques that can protect personal information. The data is no  longer be same information, it will be having some changes which is not understandable  normally that makes our security. • Location privacy: They will some privacy issues for location tracking. • Legal, social and psychological aspects of privacy: By using personal information, the hackers may use for some benefits which is  security issue. That is also becomes a legal issue for companies who had privacy breach. • Secure data outsourcing: While sending out data, we must secure it first with privacy technique and  reaching it to destination they can only utilize only main information which is useful for  them. They can’t see any personal information of the citizens.  Privacy Breach According to the 1990 U.S. Census summary data were conducted to determine how many individuals within geographically situated populations had combinations of demographic values that occurred infrequently. Combinations of few characteristics often combine in populations to uniquely or nearly uniquely identify some individuals. For example, a finding in that study was that 87% (216 million of 248 million) of the population in the United States had reported characteristics that likely made them uniquely identified based only on Zip Code, Birth Date, and Sex. Clearly, data released containing such information about these individuals should not be considered anonymous. Yet, health and other person­specific data are often publicly available in this form. Below is a demonstration of how such data can be re­identified. Reference: Sweeney, Latanya. "k­anonymity: A model for protecting privacy."  International Journal of Uncertainty, Fuzziness and Knowledge­Based Systems 10.05 (2002): 557­570.


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