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Technology Overview

Discuss About The International Computer And Communication.

Societies are experiencing exponential expansion in the amount and type of data collection that includes specific personal information, with computer technology, disk storage space and network connectivity becoming more and more affordable. Data owners operate autonomously and have limited knowledge, but it is still difficult to publish information or data that never infringe privacy, national interests and confidential. In several cases, database survival also depends on the ability of the data holder to generate anonymous data, this because not completely releasing this information might reduce the demand for data, and then again, it fails to provide proper security or protection in the release version. It may cause damage to the public or others (Chung, 2016). This study examines how Android SDK technology allows the implementation of k-anonymity as a model for privacy protection.

Privacy protection of personal data is accomplished through many technologies, such as k-anonymity, t-closeness, l-diversity, and so on, but the proposed technology is only implemented only in a laptop or in a computer system. Nowadays people are more interested in carrying mobile devices rather than lab desktops because few of the work done by some laptops can likewise be done through mobile devices such as file sharing, image and video sharing, and so on. , but sharing may lose data privacy. In order to provide protection for information, a certain end-goal is to use the policy k-anonymity, which selects k-esteem, where k-1 individual data is additionally displayed at discharge. This technology is implemented by using the Android SDK. Whenever users request for the information, despite sending the original information or data, it is sent anonymously (Run, 2012).

Android is one of the supported workstations that are widely used in the advanced mobile-phones. Android contains some Linux considerations, middleware is written in the C, libraries and APIs, and application programming which is running on the application system that integrates Java's perfect library with Apache Harmony. The Dalvik virtual machine is used by Android to run the Dalvikdex without problems. This code is usually encrypted from the Java byte-code. Also, ARM framework is the Android hardware platform. Android x 86 projects completely supports x86 and Google TV use this version (Wang, Xu and Sun, 2011). Android offers engineers a wealth of creative applications. Designers can use gadget gadgets, access regional data, basic management, for alerts, status bar alerts, etc. Engineers have complete access to the similar structured APIs that are used by the central application. Application engineering is designed to solve the problem of component reuse; any application might assign its functionality, and any other application might use these features (limited by system maintenance security requirements) (El Emam and Dankar, 2008). With the same tool, customers can replace segments. The service includes several applications, including the following: a well-defined set of applications for building applications, including text boxes, tabs, buttons, and even a built-in browser. The resource manager provides some non-code resources, such as localized graphics. Data obtained from other programs are displayed by the Notified Information Manager in the Status bar that displays all Personal alerts (Nielson and Gollmann, 2014).

K-Anonymity across an Organization to Protect the Privacy of Organizational Data

Advancements in new technology offer users many opportunities to provide data. One of them is with the help of mobile applications. For example, in case a user requests health information from the hospital, the mobile application must be used to immediately provide the user with the data. This application likewise provides security because certain parts of the data contain sensitive information. This security or protection is provided by utilizing a method named as k-anonymity. This K-Anonymous Property declares that for each record in the loss a number of k-1 individuals must be whose data is shown in the record. This method is based primarily on the concept of privacy, which measures the ability to estimate raw data of changed data. The technology known as privacy protection in data mining is a technology which delivers sensitive features to the original data and how to protect sensitive features through direct and indirect disclosures (Wang, 2018). To protect the delicate information organizations use a method called k-anonymity. The K-anonymity model compares the insurance model of information leak with the recognition that the respondent is conceivable, as the information implies. K-anonymous data mining has always been considered as a means of protecting and protecting, while the progress of portable applications is being exploited to release information exploitation results. Several organizations published micro-data for purposes such as medical, business and demographic research (Fellows, Tan and Zhu, 2010). The disclosure of this data may compromise privacy. In order to give anonymity, a few of the explicit characteristic identifiers (for example social security numbers, addresses and phone numbers) are encrypted. Some features of any method, such as gender, age, and zip code, can identify personal information when combined. The information accessible to the attacker is a serious problem. Personal data is collected daily when they purchase goods or increase their daily actions (for example financial or demographic) (Huang, 2014). Like Amazon, Flip kart and other information agencies, the site contains information about ordinary consumers (Chen and Li, 2016). Data, usually provided as publically available data, can be easily sold as well as can be utilized to combine identities with unidentified information. This data is usually included in the Excel-form, which can include name, age, postal code, DOB, and such cases mainly in medical, personal, financial and other fields. This data might be used for the research purpose. Be threatened with personal privacy. This kind of anonymity can be handed out by the mobile application. As soon as the user requests data, it can immediately provide data (Tan, 2012).

Technology Solution

With advances in technology over the past decades, medical institutions have accumulated a large amount of health-related electronic data or information. This data provides valuable resources for researchers, decision-makers, and analysts. For instance, epidemiologists can use urgent situation visits to identify outbreaks that demand further investigation and take appropriate action on time. Public health information is likewise provided to the public as part of public health awareness and education. For instance, electronic birth certificates might: provide a rich source for researchers, risk factors for baby deaths or other adverse reproductive outcomes; Provide information for advocates, healthcare providers, government and non-profit organizations. Specific local data along with child health issues as well as help with policy development same as other health departments across the whole country, such as Indiana Marion County Department of Public Health (MCPHD) gives access to public data Marts, Data Mart is a provider of internet programs birth and collection death data. The user can get two pieces of information about the characteristics, such as the birth risk factor summarized by year, census, breed, and so on (Agrawal and Kesdogan, 2013).

Privacy protection Personal data usage on hand - Develop Android applications and because of the huge increase in usage, many people are interested in performing cell phones rather than laptops because the phone is not limited to audio  or video use, but might also be utilized for several other purposes, such as file exchange, health, financial information and so on. Therefore, when user need to exchange personal information, or when companies or researchers request, information must be provided immediately without the security or privacy of the personal data. This security or privacy is provided by anonymous data, utilizing an anonymous k-value method where the value of k determines the level of anonymity. If everyone does not follow the information contained in as minimum K-1 personal information may appear simultaneously in the publication, the results will be anonymous. Progress in new technology offers users many opportunities to provide data. One of them is with the help of mobile applications. During the information exchange, certain sensitive information may leak. For example, information may leak if two bank executives or managers exchange the details or description of their branch offices and smoothly exchange health-related information between employees in different branches of the hospital (Run, 2012). For this reason, as long as sensitive information or data is exchanged, privacy is provided. This security or privacy is given by anonymous data. This kind of anonymity is providing by the k-anonymity method, which is executed or implemented like an application in the Android. K-Anonymity is the best technology that helps to release large amounts of data for business use. For example, a personal or classroom use part or all of the works in digital or hard copy is available free of charge, provided that the copy or copies are not for profit or commercial benefit production or distribution, and have a copy of this notice and all about the first page. The copyright of third-party mechanism of this work must be respected.

K-anonymity is a method used to ensure that at least k individuals cannot distinguish an individual. However, most methods for implementing k anonymization focus on improving the efficiency of k-anonymous algorithms; from a researcher point of view, the point is not to ensure the "usefulness" of anonymous data (Run et al., 2012). A new data utility metric is introduced called research value (RV) that increases current effective statistics by determining the limitation of data limits designed to improve the effectiveness of anonymous data queries. In order to anonymize a given set of original data, two algorithms are proposed that use a predetermined summary used by the information content expert as well as the corresponding learning value to evaluate the data utility of the feature as it promotes data to make sure anonymity. Additionally, a good automated algorithm is proposed that utilized for clustering and for the RV to anonymous data sets. When various attributes in the data set are large, all of the proposed algorithms can be scaled effectively (SWEENEY, 2012).

Login: This module gives users with confirmation. Once logged in, the login can be used to execute the operations that the user must have in the algorithm.

Browsing: The browser module is utilized to choose a specific Excel form file from a directory and run a k-anonymous algorithm on the file. The users-selected filename is displayed in a file path (LI and YANG, 2013).

Reset file: Use this module to access the new files or to stop ongoing algorithmic operations during operation. By clicking one button, the application will refresh and clear all fields.

Do k-anonymous: If this button is clicked, it checks if the file is not completed. Any other file than the Excel format file cannot be accepted, moreover, if other files are selected, an error message will be displayed. If the chosen file excels, it starts to perform operations on a file.

The result of the file Name: The action performed on a selected file will generate an output that will be saved in another file or folder with the similar name in a Result file or folder name field, as well as the file will be saved on a similar path as the input file. The name of the resulting file can be sent to anyone who needs it (Chen and Li, 2016).

Sensitive columns: There can be any number of sensitive columns in the selected file, and the choice is based on the user's needs. This choice is based entirely on the properties (column names) displayed in the given enter file. These properties displayed on the rotator unable to perform actions on sensitive columns (Suryadevara and Rizkalla, 2015).

A number of columns: Using this module, the set of columns might be executed by selecting the number of columns. The minimum as well as maximum values are automatically selected base on the values given by the user and sorted as per their values in this field. In this unit, only numeric values display on a spinner. When user fills in the number of columns, you can only display the number of key entry columns with numbers that can be used to change.

Repeat columns: Depending on the number of columns chosen by the user and if there are any unique values in the columns, repeat it so that others do not recognize the original value in a columns (Rao, Sheshikala and Prakash, 2017). In this unit, the properties of the string values are copied, and they are displayed in spinners in the numeric range.

Create * column: The user selects the number of unique columns that the user needs to hide. In this unit, every row is easily compared to other rows (Yuan, Wu and Lu, 2013). If there is parallel information, the information that differs from one another will be marked with an asterisk (*). The unit contains properties that have numeric values in a column.

Mail attachment: The completion of the implementation of an algorithm is saved with the result file name, and the result file can be viewed and attached by one click on the attachment button furthermore the sender button will use the desired end-user email to the Email ID (LIU and WANG, 2010).

The manner in which k anonymity applies depends on the re-identification system that people oppose. To prevent prosecutors from reassessing the situation, k-anonymity must be used. If the prosecutor's plan does not apply, it is not recommended to use k-anonymity and k-mapping must be used (or use hypothesis test method D4 approach) (Yuan, Wu and Lu, 2013). If both conditions are reasonable, k-anonymity must be used because it is the most protective. Therefore it is important to decide whether the prosecutor's plan applies (Kasurde and Bhati, 2016).

Conclusion

The study concludes that the exchange of data is very important. Data utilities need to be fully maximized while protecting private information. For each quasi-identifier combination in the k-anonymity table, minimum k-records share these values. K-anonymity protects data anonymously from switching attacks but it goes further: Due to the lack of diversity, k-anonymity can reveal information. K anonymous cannot prevent background-based attacks. Disclosure of this data can cause serious privacy issues. For example, consider all clinical and experimental data and trial articles published by individuals participating in clinical trials on the journal's website. If the person's record of these public data can be re-defined, it will violate privacy. As a result of privacy issues, such incidents can lead to a decrease in the number of people involved in the study, and if it occurs in Canada, it will violate the laws of privacy. Therefore, it is important to accurately understand the types of re-identification attacks that can be initiated on the dataset and the different ways in which anonymous data can be used correctly before being exposed. Anonymous technology can cause data distortion. Over-anonymization can reduce data quality, which makes it inappropriate for some analyzes, and may lead to errors or prejudiced results. Therefore, it is very important to balance the number of anonymities and the amount of information lost.

References

Agrawal, D. and Kesdogan, D. (2013). Measuring anonymity: the disclosure attack. IEEE Security & Privacy Magazine, 1(6), pp.27-34.

Chen, X. and Li, Y. (2016). The Causality Test of Network Technical Anonymity and Perceptive Anonymity. International Journal of Future Generation Communication and Networking, 9(3), pp.279-288.

Chen, X. and Li, Y. (2016). The Causality Test of Network Technical Anonymity and Perceptive Anonymity. International Journal of Future Generation Communication and Networking, 9(3), pp.279-288.

Chung, W. (2016). Social media analytics: Security and privacy issues. Journal of Information Privacy and Security, 12(3), pp.105-106.

El Emam, K. and Dankar, F. (2008). Protecting Privacy Using k-Anonymity. Journal of the American Medical Informatics Association, 15(5), pp.627-637.

Huang, X., Liu, J., Han, Z. and Yang, J. (2014). A new anonymity model for privacy-preserving data publishing. China Communications, 11(9), pp.47-59.

Fellows, M., Tan, X. and Zhu, B. (2010). Frontiers in algorithmics and algorithmic aspects in information and management.

Kasurde, A. and Bhati, P. (2016). Implementation of Robust Barcode Modulation Mechanism for Large Data Trans Reception Using Android Device. International Journal Of Engineering And Computer Science.

LI, W. and YANG, G. (2013). Energy-saving data aggregation algorithm for protecting privacy and integrity. Journal of Computer Applications, 33(9), pp.2505-2510.

LIU, X. and WANG, N. (2010). Survey of anonymity communication. Journal of Computer Applications, 30(3), pp.719-722.

Nielson, H. and Gollmann, D. (2014). Secure IT systems.

Rao, D., Sheshikala, M. and Prakash, R. (2017). Implementation of K-Anonymity Using Android SDK.

Run, C., Kim, H., Lee, D., Kim, C. and Kim, K. (2012). Protecting Privacy Using K-Anonymity with a Hybrid Search Scheme. International Journal of Computer and Communication Engineering, pp.155-158.

Run, C., Kim, H., Lee, D., Kim, C. and Kim, K. (2012). Protecting Privacy Using K-Anonymity Tan, P. (2012). Advances in knowledge discovery and data mining. Berlin: Springer.

with a Hybrid Search Scheme. International Journal of Computer and Communication Engineering, pp.155-158.

Suryadevara, V. and Rizkalla, M. (2015). Smartphone Based Fall Detection and Logic Testing Application Using Android SDK. Journal of Biomedical Science and Engineering, 08(09), pp.616-624.

SWEENEY, L. (2012). k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), pp.557-570.

Wang, J., Cai, Z., Li, Y., Yang, D., Li, J. and Gao, H. (2018). Protecting query privacy with differentially private k-anonymity in location-based services. Personal and Ubiquitous Computing.

Wang, Q., Xu, C. and Sun, M. (2011). Multi-dimensional k-anonymity Based on Mapping for Protecting Privacy. Journal of Software, 6(10).

Yuan, Y., Wu, X. and Lu, Y. (2013). Trustworthy computing and services. Berlin: Springer.

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My Assignment Help. Protecting Privacy With K-Anonymity And Android Technology [Internet]. My Assignment Help. 2019 [cited 27 February 2024]. Available from: https://myassignmenthelp.com/free-samples/international-computer-and-communication.

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