NMReDATA, a standard to report the NMR assignment and parameters of organic compounds

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Open access articleEven though NMR has found countless applications in the field of small molecule characterization, there is no standard file for the NMR data relevant to structure characterization of small molecules. A file format is introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the assigned chemical structure. These NMR parameters, which we shall call NMReDATA, include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times and diffusion rates. The file format is an extension of the existing SDF (Structure Data Format), which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, automating the verification of published result, and for assisting the constitution of highly needed open-source structural databases

Advances in Inference Control in Statistical Databases: An Overview

Inference control in statistical databases is a discipline with several other names, such as statistical disclosure control, statistical disclosure limitation, or statistical database protection.R egardless of the name used, current work in this very active field is rooted in the work that was started on statistical database protection in the 70s and 80s

Cardinality-based Inference Control in Sum-only Data Cubes

This paper deals with the inference problems in data warehouses and decision support systems such as on-line analytical processing (OLAP) systems

Auditing Interval-based Inference

In this paper we study the feasibility of auditing inteval - based inference. Sensitive information about individuals is said to be compromised if an accurate enough interval, called inference inteval, is obtained into which the value of the sensitive information must fall

Conditions for swappability of records in a microdata set when some marginals are fixed

Decomposable model, Disclosure control, Graphical model, Hierarchical model, Markov basis,

Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators

As databases grow more prevalent and comprehensive, database administrators seek to limit disclosure of confidential information while still providing access to data. Practical databases accommodate users with heterogeneous needs for access. Each class of data user is accorded access to only certain views. Other views are considered confidential, and hence to be protected. Using illustrations from health care and education, this article addresses inferential disclosure of confidential views in multidimensional categorical databases. It demonstrates that any structural, so data-value-independent method for detecting disclosure can fail. Consistent with previous work for two-way tables, it presents a data-value-dependent method to obtain tight lower and upper bounds for confidential data values. For two-dimensional projections of categorical databases, it exploits the network structure of a linear programming (LP) formulation to develop two transportation flow algorithms that are both computationally efficient and insightful. These algorithms can be easily implemented through two new matrix operators, cell-maxima and cell-minima. Collectively, this method is called matrix comparative assignment (MCA). Finally, it extends both the LP and MCA approaches to inferential disclosure when accessible views have been masked.confidentiality, data access, linear programming, matrix methods, disclosure risk, network models, disclosure limitation