What is a de-Anonymization attack?
In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces.
What is de-Anonymization by network operators?
De-anonymization is a technique used in data mining that attempts to re-identify encrypted or obscured information. De-anonymization, also referred to as data re-identification, cross-references anonymized information with other available data in order to identify a person, group, or transaction.
What is data anonymization example?
Data Anonymization Techniques For example, you can replace a value character with a symbol such as “*” or “x”. Pseudonymization—a data management and de-identification method that replaces private identifiers with fake identifiers or pseudonyms, for example replacing the identifier “John Smith” with “Mark Spencer”.
What is data anonymization techniques?
Data anonymization is done by creating a mirror image of a database and implementing alteration strategies, such as character shuffling, encryption, term, or character substitution. For example, a value character may be replaced by a symbol such as “*” or “x.” It makes identification or reverse engineering difficult.
What is the meaning of anonymization?
transitive verb. : to remove identifying information from (something, such as computer data) so that the original source cannot be known : to make (something) anonymous There’s an incredible amount of data in your travel profile. So they improved security and created a sophisticated way to anonymize data.—
What is pseudonym data?
Pseudonymisation means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific person without the use of additional information. Such additional information must be kept carefully separate from personal data.
What is anonymization GDPR?
Anonymisation is the process of removing personal identifiers, both direct and indirect, that may lead to an individual being identified. Once data is truly anonymised and individuals are no longer identifiable, the data will not fall within the scope of the GDPR and it becomes easier to use.
What is data masking and anonymization?
Data anonymization is also known as “data obfuscation,” “data masking,” or “data de-identification.” It can be contrasted with de-anonymization, which are techniques used in data mining that attempt to re-identify encrypted or obscured information.
Why is anonymization a challenge of cybersecurity?
Anonymity challenges The main challenge of anonymization is therefore to maintain an appropriate balance between the level of privacy and utility of the data. We have also seen that using pseudonyms is not enough, and that anonymization offers generally better guarantees of privacy.
How do you Pseudonymize data?
Compliance Methods Data masking and hashing are examples of pseudonymizing sensitive data. Data masking is the de facto standard for achieving pseudonymization. It replaces sensitive data with fictitious yet realistic data, which helps reduce data risk while preserving data utility.
What is the difference between anonymization and pseudonymization?
In short, while anonymization eliminates direct re-identification risk, pseudonymization substitutes the identifiable data with a reversible, consistent value. However, it is essential to note that anonymization may sometimes carry the risk of indirect re-identification.
Are geolocated datasets vulnerable to de-anonymization attacks?
More precisely, we have demonstrated that geolocated datasets gathering the movements of individuals are particularly vulnerable to a form of inference attack called the de-anonymization attack.
What is a de-anonymization attack?
In this work, we focus on a particular form of inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces.
Is it possible to anonymize mobility data?
The results shows that anonymizing mobility data is a difficult task. With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded.