Legal and Technical Issues of Privacy Preservation in Data Mining

2011 ◽  
pp. 1158-1163 ◽  
Author(s):  
Kirsten Wahlstrom ◽  
John F. Roddick ◽  
Rick Sarre ◽  
Vladimir Estivill-Castro ◽  
Denise de Vries
Keyword(s):  
Data Mining ◽  
Social Responsibility ◽  
Private Information ◽  
New Technologies ◽  
Ethical Issues ◽  
Sensitive Information ◽  
Personal Privacy ◽  
Customer Data ◽  
Moral Principles ◽  
Very Large Databases

To paraphrase Winograd (1992), we bring to our communities a tacit comprehension of right and wrong that makes social responsibility an intrinsic part of our culture. Our ethics are the moral principles we use to assert social responsibility and to perpetuate safe and just societies. Moreover, the introduction of new technologies can have a profound effect on our ethical principles. The emergence of very large databases, and the associated automated data analysis tools, present yet another set of ethical challenges to consider. Socio-ethical issues have been identified as pertinent to data mining and there is a growing concern regarding the (ab)use of sensitive information (Clarke, 1999; Clifton et al., 2002; Clifton and Estivill-Castro, 2002; Gehrke, 2002). Estivill-Castro et al., discuss surveys regarding public opinion on personal privacy that show a raised level of concern about the use of private information (Estivill-Castro et al., 1999). There is some justification for this concern; a 2001 survey in InfoWeek found that over 20% of companies store customer data with information about medical profile and/or customer demographics with salary and credit information, and over 15% store information about customers’ legal histories.

A Multi-Threshold Ant Colony System-based Sanitization Model in Shared Medical Environments

2021 ◽  
Vol 21 (2) ◽  
pp. 1-26
Author(s):  
Jimmy Ming-Tai Wu ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin ◽  
Qian Teng
Keyword(s):  
Data Mining ◽  
Private Information ◽  
Ant Colony ◽  
Security And Privacy ◽  
Legal Issue ◽  
Ant Colony System ◽  
Sensitive Information ◽  
Data Mining Algorithm ◽  
Useful Knowledge ◽  
Global Pandemic

During the past several years, revealing some useful knowledge or protecting individual’s private information in an identifiable health dataset (i.e., within an Electronic Health Record) has become a tradeoff issue. Especially in this era of a global pandemic, security and privacy are often overlooked in lieu of usability. Privacy preserving data mining (PPDM) is definitely going to be have an important role to resolve this problem. Nevertheless, the scenario of mining information in an identifiable health dataset holds high complexity compared to traditional PPDM problems. Leaking individual private information in an identifiable health dataset has becomes a serious legal issue. In this article, the proposed Ant Colony System to Data Mining algorithm takes the multi-threshold constraint to secure and sanitize patents’ records in different lengths, which is applicable in a real medical situation. The experimental results show the proposed algorithm not only has the ability to hide all sensitive information but also to keep useful knowledge for mining usage in the sanitized database.

Ethics of Data Mining

2011 ◽  
pp. 783-788 ◽  
Author(s):  
Jack Cook
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Financial Services ◽  
Ethical Issues ◽  
Price Change ◽  
Mining Operation ◽  
Decision Makers ◽  
Customer Relationship ◽  
Good Decision ◽  
Customer Data

Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions, even those that currently cannot be answered, is a characteristic of a good decision maker. Decision makers no longer have the luxury of making decisions based on gut feeling or intuition. Decisions must be supported by data; otherwise decision makers can expect to be questioned by stockholders, reporters, or attorneys in a court of law. Data mining can support and often direct decision makers in ways that are often counterintuitive. Although data mining can provide considerable insight, there is an “inherent risk that what might be inferred may be private or ethically sensitive” (Fule & Roddick, 2004, p. 159). Extensively used in telecommunications, financial services, insurance, customer relationship management (CRM), retail, and utilities, data mining more recently has been used by educators, government officials, intelligence agencies, and law enforcement. It helps alleviate data overload by extracting value from volume. However, data analysis is not data mining. Query-driven data analysis, perhaps guided by an idea or hypothesis, that tries to deduce a pattern, verify a hypothesis, or generalize information in order to predict future behavior is not data mining (Edelstein, 2003). It may be a first step, but it is not data mining. Data mining is the process of discovering and interpreting meaningful, previously hidden patterns in the data. It is not a set of descriptive statistics. Description is not prediction. Furthermore, the focus of data mining is on the process, not a particular technique, used to make reasonably accurate predictions. It is iterative in nature and generically can be decomposed into the following steps: (1) data acquisition through translating, cleansing, and transforming data from numerous sources, (2) goal setting or hypotheses construction, (3) data mining, and (4) validating or interpreting results. The process of generating rules through a mining operation becomes an ethical issue, when the results are used in decision-making processes that affect people or when mining customer data unwittingly compromises the privacy of those customers (Fule & Roddick, 2004). Data miners and decision makers must contemplate ethical issues before encountering one. Otherwise, they risk not identifying when a dilemma exists or making poor choices, since all aspects of the problem have not been identified.

Deep active reinforcement learning for privacy preserve data mining in 5G environments

2021 ◽  
pp. 1-8
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava ◽  
Hsing-Chung Chen
Keyword(s):  
Data Mining ◽  
Active Learning ◽  
Private Information ◽  
Pattern Mining ◽  
Research Area ◽  
High Dimensional ◽  
Data Sets ◽  
Sensitive Information ◽  
Using Data ◽  
Transactional Data

Finding frequent patterns identifies the most important patterns in data sets. Due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered an important research area in recent decades. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to hide sensitive operations and protect private information. This paper combines entropy-based active learning with an attention-based approach to effectively detect sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the decision boundaries by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve cleanup by hiding sensitive items and avoiding non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches.

scholarly journals Efficiently Hiding Sensitive Itemsets with Transaction Deletion Based on Genetic Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Chun-Wei Lin ◽  
Binbin Zhang ◽  
Kuo-Tung Yang ◽  
Tzung-Pei Hong
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Private Information ◽  
Fitness Function ◽  
Evolutionary Process ◽  
Sensitive Information ◽  
Large Database ◽  
Inherent Risk ◽  
Np Hard Problem ◽  
Very Large Database

Data mining is used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding sensitive information, which can be concerned as an NP-hard problem in sanitization process. In this paper, a compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is thus proposed. It solves the limitations of the evolutionary process by adopting both the compact GA-based (cGA) mechanism and the prelarge concept. A flexible fitness function with three adjustable weights is thus designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets with minimal side effects of hiding failure, missing cost, and artificial cost. Experiments are conducted to show the performance of the proposed cpGA2DT algorithm compared to the simple GA-based (sGA2DT) algorithm and the greedy approach in terms of execution time and three side effects.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

DATA MINING AS A MEANS OF QUALITY CONTROL IN PRODUCTION AND ORGANIZATION MANAGEMENT

2020 ◽  
Vol 1 (5) ◽  
pp. 130-138
Author(s):  
L. S. ZVYAGIN ◽  
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
New Technologies ◽  
Data Interpretation ◽  
Organization Management

The article deals with data mining (IAD), which is widely used both in business and in various studies. IAD methods are used to create new ways to solve problems of forecasting, segmentation, data interpretation, etc. The problems to be solved by creating new technologies and methods of IAD are analyzed.

Responding to Social Responsibilities and Ethics - A Study of Refractory Industry in Odisha

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Saikat Gochhait
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Ethical Issues ◽  
Business Organizations ◽  
Ethical Practices ◽  
Secondary Sources ◽  
Modern Business ◽  
Industrial Markets ◽  
Corporate Social ◽  
Profit Maximisation

Businesses work in a wide social environment in which they have a responsibility to a range of stakeholders including the community. The term Corporate Social Responsibility (CSR) refers to the responsibility that modern business organizations have to creating a healthy and prosperous society. Ethical practices in refractory marketing help marketers distinguish between right and wrong behavior. Adherence to ethics is essential in industrial markets as mutual trust among buyers and sellers is the key to long-term success. Marketing has evolved from a production-centric approach to a societal marketing approach that lays greater emphasis on the ethical issues in marketing. With the advent of globalization, corporations continue to evolve, grow in power, and influence the process of consolidation. Corporations are in positions of power that allow them to do greater damage to others when they act unethically or socially in an irresponsible manner. The rights theory encompasses a variety of ethical philosophies holding that certain human rights are fundamental and must be respected by other humans. The economic theories of the firm cannot be segregated of ethical considerations as they have crucial impact on how the firm concentrates on economic power, formulate the rules of law. Profit maximisation has always been the driving force and an undercurrent behind the development of corporate. But profit is not made in vacuum, it always has an associated cost, some of which is always externalized (Rhee, 2008). Corporate law has an ethical foundation and the debate on values necessarily revolves round the activities of the firm. This research paper on the basis of secondary sources of data collected from reports, research papers and Internet, focuses on corporate social responsibility (CSR) of TATA Group with reference to Tata Krosaki Refractories Ltd, Bajoria Group with reference to IFGL Refractories Ltd (Odisha), OCL Refractories Ltd, Sarvesh Refractories, and Manishree Refractories (Odisha). The study intends to understand the scope of corporate social responsibility and get an insight in CSR and ethical practices in the light of the case study of the refractory industries in Odisha.

scholarly journals Technology and Corporate Social Responsibility

2021 ◽  
Vol 13 (15) ◽  
pp. 8658
Author(s):  
Vojko Potocan
Keyword(s):  
Sustainable Development ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
New Technologies ◽  
Social Issues ◽  
Technological Development ◽  
Sustainable Use ◽  
Interdisciplinary Treatment ◽  
Sustainable Technologies ◽  
Corporate Social

This study examined the importance of technologies in advancing modern organizations’ corporate social responsibility (CSR). Drawing upon environmentalist and technological theories, we analyzed the shift from the traditional development of technology to the development of sustainable technologies for the further sustainable advancement of organizations. Technology has decisively influenced the development of humankind, but its research has traditionally excluded sustainable development issues. Newer technological visions have addressed the incorporation of technologies in all industries more comprehensively to solve social issues related to environmental protection and sustainable economic development. Such an orientation is followed by several conceptual solutions, such as the sustainable use of traditional technologies, development of sustainable technologies, and interdisciplinary treatment of sustainable technology to extend the CSR model. The results of our study have theoretical implications, highlighting the effects of technological development and new technologies on the course of further societal sustainable development. Practical implications include extending CSR’s Triple Bottom model with a technological dimension to improve organizations’ further sustainable operating and behavior.

E-Commerce product comparison portal for classification of customer data based on data mining

2021 ◽  
Author(s):  
Dilip Kumar Sharma ◽  
Sarika Lohana ◽  
Saurabh Arora ◽  
Ashutosh Dixit ◽  
Mohit Tiwari ◽  
...  
Keyword(s):  
Data Mining ◽  
Customer Data ◽  
Product Comparison

The Ethics of Mobile Health Technology

2017 ◽  
Vol 41 (S1) ◽  
pp. S39-S39
Author(s):  
S. Galderisi ◽  
F. Caputo
Keyword(s):  
Health Equity ◽  
Mobile Health ◽  
New Technologies ◽  
Ethical Issues ◽  
Professional Associations ◽  
Health Technology ◽  
Ethical Guidelines ◽  
High Quality ◽  
Quality Healthcare ◽  
Health Technologies

IntroductionMobile health (m-health) technology has been growing rapidly in the last decades. The use of this technology represents an advantage, especially for reaching patients who otherwise would have no access to healthcare. However, many ethical issues arise from the use of m-health. Health equity, privacy policies, adequate informed consent and a competent, safe and high quality healthcare need to be guaranteed; professional standards and quality of doctor-patient relationship in the digital setting should not be lower than those set for in-person practice.AimsTo assess advantages and threats that may arise from the wide use of m-health technologies, in order to guarantee the application of the best medical practices, resulting in the highest quality healthcare.MethodsA literature search has been conducted to highlight the most pressing ethical issues emerging from the spreading of m-health technologies.ResultsFew ethical guidelines on the appropriate use of m-health have been developed to help clinicians adopt a professional conduct within digital settings. They focus on the need for professional associations to define ethical guidelines and for physicians to take care of their education and online behavior when using m-health technologies.ConclusionsThe rapid spreading of m-health technologies urges us to evaluate all ethical issues related to its use. It would be advisable to produce an ethical code for the use of these new technologies, to guarantee health equity, privacy protection, high quality doctor-patient relationships and to ensure that m-health is not chosen over traditional care for merely economic purposes.Disclosure of interestSG received honoraria or Advisory board/consulting fees from the following companies: Lundbeck, Janssen Pharmaceuticals, Hoffman-La Roche, Angelini-Acraf, Otsuka, Pierre Fabre and Gedeon-Richter. All other authors have declared.

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