▁Pseud onym ization ▁is ▁performed ▁by ▁replacing ▁real ▁names ▁with ▁a ▁temporary ▁ID , ▁it ▁delet es ▁or ▁mask s ▁personal ▁ident ifiers ▁to ▁make ▁individuals ▁un ident ified . ▁This ▁method ▁makes ▁it ▁possible ▁to ▁track ▁the ▁individual ' s ▁record ▁over ▁time ▁even ▁though ▁the ▁record ▁will ▁be ▁updated . ▁However , ▁it ▁can ▁not ▁prevent ▁the ▁individual ▁from ▁being ▁identified ▁if ▁some ▁specific ▁combinations ▁of ▁attributes ▁in ▁data ▁record ▁indirect ly ▁identify ▁the ▁individual . ▁ ▁k - an onym ization ▁ ▁k - an onym ization ▁defines ▁attributes ▁that ▁indirect ly ▁points ▁to ▁the ▁individual ' s ▁identity ▁as ▁quasi - ident ifiers ▁( Q Is ) ▁and ▁deal ▁with ▁data ▁by ▁making ▁at ▁least ▁k ▁individuals ▁have ▁same ▁combination ▁of ▁Q I ▁values . ▁Q I ▁values ▁are ▁handled ▁following ▁specific ▁standards . ▁For ▁example , ▁the ▁k - an onym ization ▁rep laces ▁some ▁original ▁data ▁in ▁the ▁records ▁with ▁new ▁range ▁values ▁and ▁keep ▁some ▁values ▁un changed . ▁New ▁combination ▁of ▁Q I ▁values ▁prevents ▁the ▁individual ▁from ▁being ▁identified ▁and ▁also ▁avoid ▁destroy ing ▁data ▁records . ▁ ▁App lications ▁Research ▁into ▁de - ident ification ▁is ▁driven ▁mostly ▁for ▁protect ing ▁health ▁information . ▁Some ▁libraries ▁have ▁adopted ▁methods ▁used ▁in ▁the ▁health care ▁industry ▁to ▁preserve ▁their ▁readers ' ▁priv acy . ▁ ▁In ▁big ▁data , ▁de - ident ification ▁is ▁widely ▁adopted ▁by ▁individuals ▁and ▁organizations . ▁With ▁the ▁development ▁of ▁social ▁media , ▁e - commerce , ▁and ▁big