Currently, artificial intelligence/machine learning (AI/ML) and other technologies (mathematics or statistics) have been introduced into various engineering fields for data science research. We all know that in the past few decades, civil/mining and other fields have accumulated a large amount of data indoors or on-site, but because these data are directly or indirectly related to projects, enterprises, or state secrets, they have very high privacy. . In other words, it is data sensitivity and security. So, with the rapid development of AI/ML, how to do a trade-off between the scientific issues of data science and data security?
Dear Chengkai Fan ,
Data is taking over almost each and every sector, even the ones that you may not immediately relate it to. One of those is construction/civil engineering. Today, however, that data is complemented by a huge amount of information generated by sources such as building engineering logs, cranes, construction workers, earth movers and materials logistics. Before AI revolution, construction software did a great job at recording static information, such as Computer Aided Drafting (CAD) schematics, expenses, invoices, employee details, and project details. Today, on the other hand, construction professionals and civil engineers need the kind of answers hidden in unstructured data, like free-form text (for example email and Word documents), printed documents and analog sensor data.
https://www.datatrained.com/post/data-science-usage-in-civil-engineering/
Regards,
Shafagat
AI/ML are quite powerful tools in an open information, products, and services exchange marketplace. However, their usefulness is a great deal diminished under massive security restrictions. Perhaps, a relative freedom of information exchange for products and services unrelated to security restrictions can be opened and tested. That is on some limited, small scale AI/ML proliferation into the marketplace of civil/mining engineering may be still possible.
Chengkai Fan,
There are other fields that are tackling the same issue and each has adopted particular objectives depending on the type of data. For example, within the healthcare segment aggregate information is generally used.The data is disclosed A particular thorny issue on healthcare is anonymizing individual data. Possible inference attacks can be carried out on the data and obtain the particular individual.
In any trade-off analysis the important thing to do is to quantify the benefits of the ML data output. If the benefits of the output exceed the benefits of keeping the data private then the decision can be presented as an alternative.
The particulars of the trade-off analysis depends on the type of asset (e.g. trade secret, statistical data, cumulative measurements from tests, etc.)
Regards
Dear Chengkai Fan
In many circumstances, the choices are based on cost or power rather than a conflict with privacy. That is, the apparent choice between security and privacy may actually be two tradeoffs: one between security and money and one between privacy and money.
A trade-off is defined as giving up one element or quality of something in exchange for another aspect or quality. Information technology (IT) security focuses on data protection, which is basically the prevention of access to that data and the concealment of a user's identity.
Kind Regards
Qamar Ul Islam
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