Extending the Accounting Brand to Privacy Services (Retracted)

2003 ◽  
Vol 17 (2) ◽  
pp. 87-110 ◽  
Author(s):  
Marilyn M. Greenstein ◽  
James E. Hunton
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Service Providers ◽  
Law Firms ◽  
Big 5 ◽  
Good Data ◽  
Cpa Firms ◽  
Business Consultants ◽  
Data Privacy Protection ◽  
The Impact

The primary purposes of this study are to identify and validate a list of necessary skills required to ensure good data privacy protection for organizations, recognize potential privacy service providers, compare the perceived skill levels of potential providers, and test the impact of an educational brochure developed by the AICPA designed to communicate how and why members of the accounting profession are qualified to conduct privacy engagements. A total of 82 corporate managers representing 27 companies participated in a randomized between-subjects experiment where they responded to skill-related items either before or after reading the privacy brochure. Factor analysis of 17 skill items revealed four factors: technical, legal, control/assurance, and strategic. The following potential service providers were identified: law firms, CPA firms (both Big 5 and non-Big 5), security consultants, and e-business consultants. Study findings suggest that all four of the skill constructs are necessary in providing good data privacy protection. Before reading the brochure, the highest construct index means (potential providers) were as follows: technical (e-business consultants), legal (law firms), control/assurance (CPA firms and security consultants), and strategic (security consultants). After reading the brochure, CPA firms moved significantly higher on technical, legal, and strategic skills; additionally, CPA firms were ranked among the highest service providers in all areas except legal skills, where law firms continued to predominate.

A federated interpretable scorecard and its application in credit scoring

2021 ◽  
pp. 2142009
Author(s):  
Fanglan Zheng ◽  
Erihe ◽  
Kun Li ◽  
Jiang Tian ◽  
Xiaojia Xiang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Credit Scoring ◽  
Training Session ◽  
Model Performance ◽  
Parameter Tuning ◽  
Kolmogorov Smirnov ◽  
Data Privacy Protection ◽  
Tuning Process ◽  
Positive Coefficients

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

Is There a Better Option than the Data Transfer Model to Protect Data Privacy?

2019 ◽  
Vol 42 (2) ◽  
Author(s):  
Alan Toy ◽  
Gehan Gunasekara
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Transfer ◽  
Business Environment ◽  
Transfer Model ◽  
Privacy Rights ◽  
Choice Of Law ◽  
Privacy Laws ◽  
Data Privacy Protection ◽  
Extraterritorial Jurisdiction

The data transfer model and the accountability model, which are the dominant models for protecting the data privacy rights of citizens, have begun to present significant difficulties in regulating the online and increasingly transnational business environment. Global organisations take advantage of forum selection clauses and choice of law clauses and attention is diverted toward the data transfer model and the accountability model as a means of data privacy protection but it is impossible to have confidence that the data privacy rights of citizens are adequately protected given well known revelations regarding surveillance and the rise of technologies such as cloud computing. But forum selection and choice of law clauses no longer have the force they once seemed to have and this opens the possibility that extraterritorial jurisdiction may provide a supplementary conceptual basis for championing data privacy in the globalised context of the Internet. This article examines the current basis for extraterritorial application of data privacy laws and suggests a test for increasing their relevance.

Dealing with Data Privacy Protection

2003 ◽  
Author(s):  
Fritz Grupe ◽  
William Kuechler ◽  
Scott Sweeney
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Privacy Protection

Oracle Data Privacy Protection System of Virtual Database

2021 ◽  
pp. 353-363
Author(s):  
Shenglong Liu ◽  
Hongbin Zhu ◽  
Tao Zhao ◽  
Heng Wang ◽  
Xianzhou Gao ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Protection System ◽  
Virtual Database ◽  
Data Privacy Protection

Research on Uncertain Data Privacy Protection Based on K-Anonymity

2012 ◽  
Vol 4 (15) ◽  
pp. 140-149 ◽  
Author(s):  
Yang Jing ◽  
Ren Xiangmin ◽  
Zhang Jianpei ◽  
Wang Kechao
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Uncertain Data ◽  
Data Privacy Protection

Data Privacy Protection for Edge Computing of Smart City in a DIKW Architecture

2019 ◽  
Vol 81 ◽  
pp. 323-335 ◽  
Author(s):  
Yucong Duan ◽  
Zhihui Lu ◽  
Zhangbing Zhou ◽  
Xiaobing Sun ◽  
Jie Wu
Keyword(s):  
Smart City ◽  
Privacy Protection ◽  
Data Privacy ◽  
Edge Computing ◽  
Data Privacy Protection
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