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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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Measuring Disclosure Risk and Data Utility for Flexible Table Generators

References Antal, L., N. Shlomo, and M. Elliot. 2014. “Measuring Disclosure Risk with Entropy in Population Based Frequency Tables.” In PSD’2014 Privacy in Statistical Databases, edited by J. Domingo-Ferrer, 62-78. Berlin: Springer. Cover, T.M. and J.A. Thomas. 2006. Elements of Information Theory, 2nd ed. New York: Wiley. Dalenius, T. and S.P. Reiss. 1982. “Data Swapping: A Technique for Disclosure Control.” Journal of Statistical Planning and Inference 7: 73-85. Doyle, P., J.I. Lane, J

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Content of Disclosures Concerning Control over Financial Reporting: The Perspective of Polish Listed Companies

Fearnley, S. (2004). A methodology for analysing and evaluating narratives in annual reports: a comprehensive descriptive profile and metrics for disclosure quality attributes. Accounting Forum , 28 (3): 205–236, Bronson, S.N., Carcello, J.V. and Raghunandan, K. (2006). Firm Characteristics and Voluntary Management Reports on Internal Control. Auditing: A Journal of Practice & Theory , 25 (2): 25–39, Cearns, K. and Shah, S.P. (2013). Narrative reporting reform

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Disclosures on Control over Financial Reporting: The Reporting Practice of Banks Listed on The Warsaw Stock Exchange

References Banaszkiewicz, J., Bem, T., Bryl, A., Górnik, J., Kozera, I., Leski, J., Łapiński, G., Wietrzyk, A., Witalis, M. (2003). Audyt wewnętrzny spojrzenie praktyczne. Warszawa: Stowarzyszenie Księgowych w Polsce. Bauwhede, H.V., Willekens, M. (2008). Disclosure on Corporate Governance in the European Union. Corporate Governance: An International Review, 16 (2), 101-115. Bedard, J. C. (2009). Corporate Governance and Internal Control over Financial Reporting: A Comparison of Regulatory Regimes. The

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Disclosure-Protected Inference with Linked Microdata Using a Remote Analysis Server

Loong, B. (2012). Confidentialising the Output of a Survival Analysis in a Remote Analysis System (to appear). Journal of Privacy and Confidentiality, 4, 127-154. Reiter, J. (2002). Satisfying Disclosure Restrictions with Synthetic Data Sets. Journal of Official Statistics, 18, 511-530. Shao, J. and Tu, D. (1995). The Jackknife and Bootstrap. Hoboken, NJ: John Wiley and Sons. Shlomo, N. (2007). Statistical Disclosure Control Methods for Census Frequency Tables. International Statistical Review, 75, 199-217. DOI : http

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Prospects for Protecting Business Microdata when Releasing Population Totals via a Remote Server

/2017/7_gconfid.pdf (accessed January 2019). Thompson, G., S. Broadfoot, and D. Elazar. 2013. “Methodology for the Automatic Confdentialisation of Statistical Outputs from Remote Servers at the Australian Bureau of Statistics.” UNECE Work Session on Statistical Data Confidentiality , Ottawa, October. Available at: (accessed January 2019). Yancey, W.E., W.E. Winkler, and R.H. Creecy. 2002. “Disclosure Risk Assessment in Perturbative Micro-data Protection.” In Inference Control

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Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

Association 88: 1013–1020. Domingo-Ferrer, J. and V. Torra. 2001. “A Quantitative Comparison of Disclosure Control Methods for Microdata.” Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies , edited by P. Doyle, J. Lane, J. Theeuwes, and L. Zayatz. 111–134, Eurostat. Domingo-Ferrer, J., J.M. Mateo-Sanz, and T. Torra. 2001. “Comparing sdc Methods for Microdata on the Basis of Information Loss and Disclosure.” Proceedings of ETKNTTS 2001: Eurostat, Luxembourg June 18–20, 2001. 807–826. Luxembourg: Eurostat

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SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

from shared databases. In International Conference on Management of Data , pages 665–676, 2007. [48] F. Prasser, F. Kohlmayer, and K. A. Kuhn. The importance of context: Risk-based de-identification of biomedical data. Methods of information in medicine , 55(4):347–355, 2016. [49] J. R. Quinlan. C4.5: Programs for Machine Learning . Morgan Kaufmann Publishers Inc., 1993. [50] F. Ritchie and M. Elliott. Principles-versus rules-based output statistical disclosure control in remote access environments. IASSIST Quarterly , 39(2):5–13, 2015

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A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

: 2834-2844. Doi: Domingo-Ferrer, J. and V. Torra. 2001. “A Quantitative Comparison of Disclosure Control Methods for Microdata.” In Confidentiality, disclosure, and data access: Theory and practical applications for statistical agencies, edited by P. Doyle, J. Lane, J. Theeuwes, and L. Zayatz. 111-133. Elsevier. Available at: (accessed August 2016). Domingo-Ferrer, J. and V. Torra. 2004

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The ESM and the Principle of Transparency


This note analyses a peculiar feature of the ESM, namely the lack of an acceptable set of standards for the fundamental democratic principle of transparency. Moving from the particular nature of this mechanism, we will highlight the most critical concerns connected to secrecy, confidentiality and inviolability of documents, looking not only at the ESM Treaty but also at relevant documents approved by its bodies (in particular the Code of Conduct and the By-Laws).

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