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From Confidentiality to Work Privacy – A Transformation in a Person’s Responsibility

Abstract

The confidentiality clause and the service secret are two means coming from different branches of law, public and private, but they have the same goal – to protect information, the component of a person’s patrimony, which is a more and more important issue in the world we live in. The protections provided by the two ways are different in terms of the gravity of the penalty they may involve and for this reason they may be used with discrimination, proportionally with the importance of the protected object. But in the present conditions when the information is sancta sanctorum, only this responsibility in punishment tends to dim, those interested in providing the protection of information seek for most effective and efficient punishment.

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Enhancing a Traditional Health Care System of an Organization for Better Service with Agent Technology by Ensuring Confidentiality of Patients’ Medical Information

Abstract

Agent technology is one of the widely adapted technologies for developing applications that deliver e-Services. Ensuring confidentiality of the patients’ data in e-health care systems remains a serious challenge. Many large enterprises provide in-house health care services free of cost for their employees and their dependents as a competitive benefit to prevent employees turnover and also to maintain healthy and productive human resource. This paper proposes enhancements to the traditional health care system of an organization so that it provides better services with respect to users’ satisfaction. The requirements identification of the system proposed and the evaluation of the new system are done using a feedback model. The new system proved to be mutually beneficial to employees and employers in terms of saving time and cost and thus it enhances productivity.

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The Method of Evaluation Degree of Changes Confidentiality Attribute of Information Asset Inside ICT System / Metoda Oceny Stopnia Zmian Atrybutu Poufności Zasobu Informacyjnego w Systemie Teleinformatycznym

Abstract

The work presents the proposition of inference system that evaluates the possibility of information confidentiality attribute violation. Crisp and fuzzy variables have been defined. Values of output variables from fuzzy logic system have been presented. The way of counting the modified value of information confidentiality attribute has been described.

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Multiply-Imputed Synthetic Data: Advice to the Imputer

. Available at: http://fcsm.sites.usa.gov/files/2014/05/2003FCSM_Harel.pdf (accessed August 2017). Karr, A.F., C.N. Kohnen, A. Oganian, J.P. Reiter, and A.P. Sanil. 2006. “A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality.” The American Statistician 60: 224–232. Doi: http://dx.doi.org/10.1198/000313006X124640 . Li, F., M. Baccini, F. Mealli, E.Z. Zell, C.E. Frangakis, and D.B. Rubin. 2014. “Multiple Imputation by Ordered Monotone Blocks, with Applications to the Anthrax Vaccine Adsorbed Trial.” Journal of Computational and

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Addressing Disclosure Concerns and Analysis Demands in a Real-Time Online Analytic System

References Chipperfield, J. and Yu, F. (2011). Protecting Confidentiality in a Remote Analysis Server for Tabulation and Analysis of Data. Joint UNECE/Eurostat work session on statistical data confidentiality (Tarragona, Spain, 26-28 October 2011). United Nations Economic Commission for Europe (UNECE). Conference of European Statisticians, European Commission Statistical Office of the European Union (Eurostat). Dalenius, T. and Reiss, S. (1982). Data-swapping: A Technique for Disclosure Control. Journal of Statistical

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UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Preventing Leakage in MapReduce. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security , pages 1570–1581, 2015. [38] Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa. Oblivious multi-party machine learning on trusted processors. In 25th USENIX Security Symposium (USENIX Security 16) , 2016. [39] Raluca Ada Popa, Catherine Redfield, Nickolai Zeldovich, and Hari Balakrishnan. CryptDB: protecting confidentiality with encrypted query processing. In Proceedings of

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Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage

References 1. Hudic, A., S. Islam, P. Kieseberg, S. Rennert, E. R. Weippl. Data Confidentiality Using Fragmentation in Cloud Computing. – International Journal of Pervasive Computing and Communications, Vol. 9 , March 2013, Issue 1, pp. 37-51 2. Talib, A. M. Ensuring Security, Confidentiality and Fine-Grained Data Access Control of Cloud Data Storage Implementation Environment. – Journal of Information Security, 2015, Issue 6, pp. 118-130. 3. Liu, C., R. Ranjan, C. Yang, X. Zhang, L. Wang, J. Chen. MuR-DPA: Top-Down Levelled Multi

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Data Smearing: An Approach to Disclosure Limitation for Tabular Data

: Evaluation of Hierarchical Bayesian Imputation Models.” Journal of Official Statistics 25: 245-268. Holan, S., D. Toth, M. Ferreira, and A. Karr. 2010. “Bayesian Multiscale Multiple Imputation With Implications for Data Confidentiality.” Journal of the American Statistical Association 105: 564-577. Lambert, D. 1993. “Measures of Disclosure Risk and Harm.” Journal of Official Statistics 9: 313-331. Reiter, J. 2002. “Satisfying Disclosure Restrictions with Synthetic Data Sets.” Journal of Official Statistics 18: 531

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Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation

., Sinha, B.K., and Zayatz, L. (2011). Statistical Properties of Multiplicative Noise Masking for Confidentiality Protection. Journal of Official Statistics, 27, 527-544. R Development Core Team (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at: www.R-project.org/. Raghunathan, T.E., Reiter, J.P., and Rubin, D.B. (2003). Multiple Imputation for Statistical Disclosure Limitation. Journal of Official Statistics, 19, 1

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Statistical Disclosure Limitation in the Presence of Edit Rules

: 160-183. DOI: http://dx.doi.org/10.1111/j.1751-5823.2011.00140.x. De Waal, T., J. Pannekoek, and S. Scholtus. 2011. Handbook of Statistical Data Editing and Imputation. Hoboken, NJ: Wiley. Defays, D. and P. Nanopoulos. 1993. “Panels of Enterprises and Confidentiality: The Small Aggregates Method.” In Proceedings of the 1992 Symposium on Design and Analysis of Longitudinal Surveys, November 2-4, 1992, 195-204. Ottawa, Ontario, Canada. Available at: http://www.researchgate. net/publication/243784453_Panels_of_enter prises_and_confidentiality

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