Methods to control disclosure risk of synthetic data created by National Statistics Agencies

Objectives With the recent explosion of interest in using synthetic data (SD) for disclosure control many NSAs are releasing, or considering releasing. synthetic versions of their administrative data. This presentation will review the methods that NSAs can use to limit the disclosure risk of any planned release of synthetic data. Methods This paper will review the ways in which methods of creating can be adapted to control the disclosure risk that could arise by the release of such data either to trusted researchers or to a wider group. Methods that will be evaluated will include: • The use of Statistical Disclosure Control (SDC) methods on the synthetic data before its release • Selecting methods producing low fidelity synthetic data • Adapting the synthesis method until it satisfies measures of disclosure risk • Incoporating differential privacy (DP) into the method of creating synthetic data Results NSAs can use different methods to create SD based on real data (RD); see e.g. https://unece.org/info/publications/pub/373531. Tthe disclosure risk of SD depends on the context of its release, to whom, in what environment etc. Even if the planned method of release ensures low disclosure risk, NSAs will want to know what the disclosure risk might be if the SD got into the wrong hands. The SD can reveal that an identified person is in the RD (identity disclosure) or can disclose information about other measures for an individual that are part of the RD. Measures of identity disclosure and attribute disclosure are described. Results will be presented on the disclosure risk of examples of SD created for real examples by the methods 1 to 4. Conclusion Each of the methods 1 to 4 have strengths and weaknesses. Methods 2 and 4 will be ruled out for many applications because of poor fidelity to the RD. A practical way forward is suggested by combining methods 1 and 3

Improvement in transparency and disclosure in the ISE: Did IFRS adoption and corporate governance principles make a difference

The purpose of this study is to investigate if the transparency and disclosure level of a sample of Istanbul Stock Exchange firms is enhanced by the promulgation of a set of local Corporate Governance (CG) Principles and by the voluntary adoption of the International Financial Reporting Standards (IFRS), an international best-practice. The Capital Market Board’s CG principles are promulgated on a “comply or explain” basis and have been effective since the fiscal year 2004. Fırst, the year 2003 Transparency & Disclosure (TD) index previously created in collaboration with Standard and Poor’s is replicated for the fiscal year 2004. Using this short panel data of transparency and disclosure scores for our sample of 52 large and liquid Istanbul Stock Exchange firms, the improvement in the scores over the two years is measured. Second, with appropriate control variables in the model, we analyze the determinants of the significant improvement. We use the voluntary adoption of IFRS as an indicator of and commitment to TD, and find that the scores and their relationship with performance are higher in early adaptors. We then create a parsimonious 3-attribute Commitment-to-Better-Disclosure Index and observe a high correlation between the two indices. Finally, using a matched pairs design and controlling for IFRS adoption, we are able to attribute the improvement in the TD scores to the CG principles. The paper finally scores the sample firms’ Compliance Report and presents some preliminary statistics on the first year compliance level of ISE firms with these local CG principles. The study should be of interest to researchers, managers, analysts, boards, policy makers and regulators at a time when debate on convergence to IFRS and the impact of local CG guidelines has become intense

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

When Leaders Are Not Who They Appear: The Effects of Leader Disclosure of a Concealable Stigma on Follower Reactions

Two studies examined follower reactions to disclosure of concealable stigma (i.e., transgender identity) by a leader. Using 109 employed participants, Study 1 showed followers rated leaders disclosing a stigma less likable and effective. This effect was both direct and indirect through relational identification with the leader. Using 206 employed participants, Study 2 found when a leader\u27s stigma was involuntarily found out and disclosed later they received lower ratings of likability and effectiveness compared to leaders who voluntarily came out and disclosed earlier. Method (found out vs. came out) and timing of disclosure (later vs. earlier) had direct relationships with ratings of likability and effectiveness and method of disclosure had an indirect relationship with the outcomes via relational identification