De-anonymization: Difference between revisions
While this is my first time contributing, while it may be irrelevant, or maybe in some mistakes I may have did, it still fits the article about De-anonymization and how anonymized data is processed. My apologies for the mistake I may have did. |
→How it works: changed the definition to one that is more precise. |
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De-anonymization is | De-anonymization is the process or final state of revealing the true identity of an anonymous or pseudonymous person. All data linked to the anonymous or pseudonymous entity can then be connected to the true identity. | ||
==How it works== | ==How it works== | ||
The core of de-anonymization involves making inferences to connect different types of obfuscated data, sometimes even across platforms. | The core of de-anonymization involves making inferences to connect different types of obfuscated data, sometimes even across platforms. | ||
== How data is anonymized == | ==How data is anonymized== | ||
{{Notice|Note from Collaborator: While maybe irrelevant, it is important to understand how data is collected when it comes to it being anonymized.}} | {{Notice|Note from Collaborator: While maybe irrelevant, it is important to understand how data is collected when it comes to it being anonymized.}} | ||
Anonymization, in practice, also involves around collecting user data that is said to be "aggregated/de-identified basis" which involves the usage of [[wikipedia:K-anonymity|k-anonymity]]. There are also forms of data collection that also used in different methods such as [[wikipedia:T-closeness|''t''-closeness]], [[wikipedia:L-diversity|''l''-diversity]], and [[wikipedia:Differential_privacy|differential privacy]], however there are other forms of data collection that is also used, which have yet to be disclosed to the customers. | Anonymization, in practice, also involves around collecting user data that is said to be "aggregated/de-identified basis" which involves the usage of [[wikipedia:K-anonymity|k-anonymity]]. There are also forms of data collection that also used in different methods such as [[wikipedia:T-closeness|''t''-closeness]], [[wikipedia:L-diversity|''l''-diversity]], and [[wikipedia:Differential_privacy|differential privacy]], however there are other forms of data collection that is also used, which have yet to be disclosed to the customers. | ||
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{{reflist}}<ref>{{Cite book |last=Narayanan & Shmatikov |first=Arvind & Vitaly |title=How To Break Anonymity of the Netflix Prize Dataset |date=November 11, 2006 |publisher=The University of Texas at Austin |location=United States, Taxes, Austin.}}</ref> | {{reflist}}<ref>{{Cite book |last=Narayanan & Shmatikov |first=Arvind & Vitaly |title=How To Break Anonymity of the Netflix Prize Dataset |date=November 11, 2006 |publisher=The University of Texas at Austin |location=United States, Taxes, Austin.}}</ref> | ||
[[Category:Common terms]] | [[Category:Common terms]] | ||
<references /> | |||
Latest revision as of 08:59, 26 August 2025
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De-anonymization is the process or final state of revealing the true identity of an anonymous or pseudonymous person. All data linked to the anonymous or pseudonymous entity can then be connected to the true identity.
How it works[edit | edit source]
The core of de-anonymization involves making inferences to connect different types of obfuscated data, sometimes even across platforms.
How data is anonymized[edit | edit source]
Note from Collaborator: While maybe irrelevant, it is important to understand how data is collected when it comes to it being anonymized.
Anonymization, in practice, also involves around collecting user data that is said to be "aggregated/de-identified basis" which involves the usage of k-anonymity. There are also forms of data collection that also used in different methods such as t-closeness, l-diversity, and differential privacy, however there are other forms of data collection that is also used, which have yet to be disclosed to the customers.
Why it is a problem[edit | edit source]
Many privacy policies describe the disclosure of anonymized data to third parties in an effort to "limit unwarranted data collection". However, de-anonymization circumvents these privacy measures, allowing these third parties to engage in practices such as data sales or targeted advertising as normal. This is however, an issue when it comes to privacy, as an adversary (e.g telemarketer) will be able to conduct an research on those records in order to attempt to reveal the data that is aggregated.[1]
Examples[edit | edit source]
- ↑ Narayanan & Shmatikov, Arvind & Vitaly (November 11, 2006). How To Break Anonymity of the Netflix Prize Dataset. United States, Taxes, Austin.: The University of Texas at Austin.