Example of inference attack on GUM historydoi:http://dx.doi.org/10.1136/bmj.321.7275.1510BMJ
When drilling for data via SQL injection there are three classes of attack – inband, out-of-band and the relatively unknown inference attack. Inband attacks extract data over the same channel between the client and the web server, for example, results are embedded in a web page via a uni...
72 Under current ontology storage schema, some problems such as difficult maintenance, low query andinferencespeed may appear after OWL ontology storing in relational database. 73 In this paper, the test model of fuzzy hypothesis for the period analysis of the runoff process over a number of yea...
Fig. 1: Example cases of simplified analytical inferences.We perform case studies on networks with mutualistic (parameter setting: B = 0.1, C = 1, K = 5, D = 5, E = 0.9, H = 0.1) (a), gene regulatory (parameter setting:
A membership inference attack receives as input a trained model and an example from the data distribution, and predicts if that example was used to train the model. Unfortunately as noted by recent work [44, 69], many prior membership inference attacks use an incomplete evaluation methodology ...
Table 4 The results of multivariable MR analysis on the impact of plasma protein and oral cancer. MR, mendelian randomization Full size table Discussion There are some research findings regarding the relationship between plasma proteins and oral cancer through searching the literature database. Some st...
An inference layer in Computer Science is a component that performs processing, calculation, simulation, prediction, reasoning, and decision-making based on data. It includes modules for data processing, monitoring, behavior analysis, and decision-making. The inference layer enhances the functionality an...
To support COVID-19 pandemic planning, we develop a model-inference system to estimate epidemiological properties of new SARS-CoV-2 variants of concern using case and mortality data while accounting for under-ascertainment, disease seasonality, non-pharmaceutical interventions, and mass-vaccination. Appl...
All of this means that while pseudonymization is a necessary technique in preventing the casual revelation of sensitive data, for example during database training or during research, pseudononymised data still has to be handled in the same secure regime as the original data. ...
we note that our treatment of confidentiality is in the spirit of various other work, e.g., already early ones on statistical database security [8] and about non-interference of general program execution [9], together with the rich elaborations of follow-up studies, which for example are co...