- 1 What is Data Authenticity?
- 2 Why is it so critically important?
- 3 How To Implement Data Authenticity
- 4 References
What is Data Authenticity?
Digital data can be assumed to be authentic if it is provable that it has not been corrupted after its creation. In a strong sense, any processing means corruption, that is digital data to be authentic must be only the outcome of an acquisition process of a real world scene without any successively processing; but in a wide sense, authentic data must accordingly represent a real world scene and even if some processing has been probably applied the meaning of the scene must not be modified. Data authenticity also means that a digital object is indeed what it claims to be or what it is claimed to be.
Why is it so critically important?
This is fundamental to the digital research archive and the digital objects that are stored on the archive. The institution should be able to trust that the digital object stored has not been modified since the initial deposit by unauthorized entities. Future researchers should be assured that the digital object in the archive is authentic and is what it claims to be.
Data authenticity should be able to answer the following questions reliably:
- Was the digital object authored by the entity that claims to have to authored the object?
- Has the digital object changed in the intervening period since initial deposit?
In short, data authenticity ensures the digital provenance of the digital object through it's entire lifetime in the digital research archive.
Please read this short unpublished paper that attempts to answer the data authenticity problem:
Another critical aspect to consider is: Can an academic research institution trust a 3rd party to apply due diligence with the authenticity of the digital objects it is given?
How To Implement Data Authenticity