Trus­ted Data Hub

The joint analy­sis and use of data from diffe­rent parties, e.g. from machi­nes, usually fails due to issues of confi­den­tia­lity, compe­ti­tion and direct compe­ti­tion. The “Trus­ted Data Hub”, a novel combi­na­tion of hard­ware and soft­ware tech­no­lo­gies, provi­des the solu­tion to protect company secrets while sharing data for AI-based analysis.

The MPC allows insti­tu­ti­ons to carry out analy­ses of private data held by seve­ral other insti­tu­ti­ons without ever disclo­sing the data. In other words, MPC is a tech­ni­que where calcu­la­ti­ons and data analy­sis are perfor­med with input from various parties. The parties are not given insight into the data provi­ded by the other parties – only the results of the data analy­sis are shared.

The Trus­ted Data Hub solu­tion guaran­tees that the parti­ci­pa­ting parties only receive the results, but no infor­ma­tion about the input of others.

Many appli­ca­ti­ons in verti­cal domains can bene­fit from the Trus­ted Data Hub. An important exam­ple is the area of predic­tive main­ten­ance. To imple­ment this, machine manu­fac­tu­r­ers usually need access to raw data from multi­ple users. At the same time, data owners do not simply want to disc­lose their trade secrets or machine-gene­ra­­ted data (MGD).

The Trus­ted Data Hub enables analy­sis of the combi­ned vulnerable data from diffe­rent compa­nies without losing control of their data. At the same time, machine manu­fac­tu­r­ers can signi­fi­cantly improve their products and services by evalua­ting or analy­zing the combi­ned data of their custo­mers. For exam­ple, machine owners bene­fit from impro­ved machine avai­la­bi­lity and machine settings or from redu­ced wear and tear.

In contrast to all other exis­ting solu­ti­ons, the Trus­ted Hub is comple­tely inde­pen­dent of the analy­tic tools used. While offe­ring the same accu­racy and runtime, the Trus­ted Hub supports not only Python, but also all other ML frame­works, such as Rapidmi­ner, Knime, H2O and Matlab.

The Trus­ted Hub is rela­tively easy to inte­grate into exis­ting systems, so it addres­ses the issue of data protec­tion without impac­ting the appli­ca­tion and custo­mer expe­ri­ence. The Trus­ted Hub supports all data types (i.e. nume­ric and cate­go­ri­cal data) and all ML func­tions (i.e. linear and non-linear func­tions) and is ther­e­fore compa­ti­ble with all types of ML algorithms.

Further­more, the Trus­ted Hub not only protects the data, but can also protect the ML algo­rithm itself, i.e. only the crea­tor of the ML algo­rithm can access the code.

The imple­men­ta­tion and setup of the Trus­ted Hub is very fast and uncom­pli­ca­ted. Further­more, no indi­vi­dual adapt­a­tion is necessary.