分布式并行数据库(DPD)在所有传统的以及新兴的数据库研究领域中发表论文,包括:数据集成、数据共享、安全和隐私、事务管理、流程和工作流管理、信息提取、查询处理和优化、分析大型数据集的挖掘和可视化、存储、数据碎片,放置和分配复制协议、可靠性、容错、持久性、保留、性能和可伸缩性以及各种通信和传播平台及中间件的使用。 官网地址:http://dblp.uni-trier.de/db/journals/dpd/

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With the advent of generative modeling techniques, synthetic data and its use has penetrated across various domains from unstructured data such as image, text to structured dataset modeling healthcare outcome, risk decisioning in financial domain, and many more. It overcomes various challenges such as limited training data, class imbalance, restricted access to dataset owing to privacy issues. To ensure the trained model used for automated decisioning purposes makes a fair decision there exist prior work to quantify and mitigate those issues. This study aims to establish a trade-off between bias and fairness in the models trained using synthetic data. Variants of synthetic data generation techniques were studied to understand bias amplification including differentially private generation schemes. Through experiments on a tabular dataset, we demonstrate there exist a varying levels of bias impact on models trained using synthetic data. Techniques generating less correlated feature performs well as evident through fairness metrics with 94\%, 82\%, and 88\% relative drop in DPD (demographic parity difference), EoD (equality of odds) and EoP (equality of opportunity) respectively, and 24\% relative improvement in DRP (demographic parity ratio) with respect to the real dataset. We believe the outcome of our research study will help data science practitioners understand the bias in the use of synthetic data.

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While digital divide studies primarily focused on access to information and communications technology (ICT) in the past, its influence on other associated dimensions such as privacy is becoming critical with a far-reaching impact on the people and society. For example, the various levels of government legislation and compliance on information privacy worldwide have created a new era of digital divide in the privacy preservation domain. In this article, the concept "digital privacy divide (DPD)" is introduced to describe the perceived gap in the privacy preservation of individuals based on the geopolitical location of different countries. To better understand the DPD phenomenon, we created an online questionnaire and collected answers from more than 700 respondents from four different countries (the United States, Germany, Bangladesh, and India) who come from two distinct cultural orientations as per Hofstede's individualist vs. collectivist society. However, our results revealed some interesting findings. DPD does not depend on Hofstede's cultural orientation of the countries. For example, individuals residing in Germany and Bangladesh share similar privacy concerns, while there is a significant similarity among individuals residing in the United States and India. Moreover, while most respondents acknowledge the importance of privacy legislation to protect their digital privacy, they do not mind their governments to allow domestic companies and organizations collecting personal data on individuals residing outside their countries, if there are economic, employment, and crime prevention benefits. These results suggest a social dilemma in the perceived privacy preservation, which could be dependent on many other contextual factors beyond government legislation and countries' cultural orientation.

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While digital divide studies primarily focused on access to information and communications technology (ICT) in the past, its influence on other associated dimensions such as privacy is becoming critical with a far-reaching impact on the people and society. For example, the various levels of government legislation and compliance on information privacy worldwide have created a new era of digital divide in the privacy preservation domain. In this article, the concept "digital privacy divide (DPD)" is introduced to describe the perceived gap in the privacy preservation of individuals based on the geopolitical location of different countries. To better understand the DPD phenomenon, we created an online questionnaire and collected answers from more than 700 respondents from four different countries (the United States, Germany, Bangladesh, and India) who come from two distinct cultural orientations as per Hofstede's individualist vs. collectivist society. However, our results revealed some interesting findings. DPD does not depend on Hofstede's cultural orientation of the countries. For example, individuals residing in Germany and Bangladesh share similar privacy concerns, while there is a significant similarity among individuals residing in the United States and India. Moreover, while most respondents acknowledge the importance of privacy legislation to protect their digital privacy, they do not mind their governments to allow domestic companies and organizations collecting personal data on individuals residing outside their countries, if there are economic, employment, and crime prevention benefits. These results suggest a social dilemma in the perceived privacy preservation, which could be dependent on many other contextual factors beyond government legislation and countries' cultural orientation.

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