Joint Summer School ERS-IASC, ECAS and SIS-CLADAG Clustering, Data Analysis and Visualization of Complex Data May 21-25, 2018, Catania (Italy)
The course is intended to achieve postgraduate training in special areas of statistics for both researchers and professional data analysts. The focus is on classification and clustering methods with particular emphasis on modern high-dimensional data sets (MHDS). MHDS have recently emerged because of the fast improvement in data acquisition, storage and processing. The availability of massive data sets are of large interest also in machine learning, data science and computer science. It applies in many contexts such as biological experiments, financial markets, astronomy, etc. Classification and clustering play a key role in this new paradigm to discover the inhomogeneous structure often underlying these data. Starting from basic concepts, the course will introduce the audience to novel techniques and software through extensive applications to real data.
Kopš 2017. gada 20. novembra mums ļoti pietrūkst Rutas Krievkalnas optimisms, labsirdība, koleģialitāte, jo mūžības ceļu aizgājusi ilggadēja un aktīva Latvijas Statistiķu Asociācijas biedre. Izsakām visdziļāko līdzjūtību un skumju brīdī esam kopā ar tuviniekiem.
Lasījums notiks 2017. gada 13. novembrī plkst. 17.00 Aspazijas bulv. 5, 320. auditorijā.
Dalībnieki: Latvijas Statistiķu asociācijas biedri, Centrālās statistikas pārvaldes darbinieki, Latvijas Universitātes Doktorantūras skolās studējošie, Latvijas augstskolu mācībspēki un studenti, citi interesenti.
This three day course will introduce basic and advanced concepts of statistical disclosure control, privacy and confidentiality. The topics covered include the motivation of statistical disclosure control in terms of disclosure risk scenarios and types of disclosure risk; measuring disclosure risk for traditional outputs: microdata and tabular data; common methods of statistical disclosure control applications; the impact of statistical disclosure control methods on utility. In addition, we introduce differential privacy which is a definition arising in computer science which provides formal and quantifiable guarantees of disclosure risk. This definition is becoming more important to statistical agencies who are moving towards more advanced and online modes of data dissemination. The course also covers legal and ethical issues, examples of best practice and applications in statistical offices or health authorities, as well as workshops on differential privacy, anonymisation and controlled access and generating synthetic data.
This is an exciting and unique course bringing together experts around the world to deliver the latest developments in privacy and confidentiality which will be pitched at the post-graduate level and for those working at statistical agencies.
By the end of this course, participants will understand the motivation of statistical disclosure control and the issues in applying statistical disclosure control methods in order to protect the confidentiality of respondents. Participants should be able to evaluate and critique the different statistical disclosure control methods depending on the type of statistical output with respect to the amount of protection afforded and the impact on the utility of the protected data. Participants should understand the definition of differential privacy and how it can be used for guaranteeing the confidentiality of statistical data. Participants should be able to apply advanced methods of statistical disclosure control based on new platforms of data dissemination.