The phrase "data sanitization" is correct and usable in written English.
It can be used when discussing the process of cleaning or removing sensitive information from data sets to protect privacy and security.
Example: "Before sharing the dataset, we need to ensure proper data sanitization to remove any personally identifiable information."
Alternatives: "data cleansing" or "data scrubbing."
Stronger data poisoning attacks break data sanitization defenses.
See Secure Computing for a complete description of University policy regarding data sanitization.
Data sanitization is the process of deliberately, permanently, irreversibly removing or destroying the data stored on a memory device.
For the purpose of data sanitization, all packets without a valid IPv4 header are deemed invalid and discarded.
As of November, 2014, Mozilla engineers are working on improving the data sanitization process in order to release database dumps again.
As such, a quick format doesn't count as data sanitization for devices intended to be discarded.
It is a type of information sanitization whose intent is privacy protection.
Sliding window approach used in SWA make it quite scalable but, still in case of big data the sanitization time is huge.
Fig. 2 Effect of varying data size on sanitization time with single computing node (MaxFIA) Fig. 3 Effect of varying data size on sanitization time with single computing node (SWA).
Fig. 4 Effect of varying data size on sanitization time with varying number of computing nodes in cluster (MaxFIA) Fig. 5 Effect of varying data size on sanitization time with varying number of computing nodes in cluster (SWA).
A couple of new additions to the Android Autofill Framework for developers who write password managers will also make life a bit easier for users, though right now, the focus here is on better data set filtering, input sanitization and a compatibility mode that will allow password managers to work with apps that don't have built-in support for Autofill yet.
