[PDF] k-Anonymity: A Model for Protecting Privacy | Semantic Scholar (2024)

FAQs

What is the k-anonymity approach? ›

To use k-anonymity to process a dataset so that it can be released with privacy protection, a data scientist must first examine the dataset and decide whether each attribute (column) is an identifier (identifying), a non-identifier (not-identifying), or a quasi-identifier (somewhat identifying).

A dataset is considered K anonymous when, for every combination of identifying attributes in a dataset, there are at least “K minus 1” other people with the same attributes. In other words, the data is not unique to a certain individual, and therefore can't be used to identify them.

K Anonymity ensures that no 1 person's information can be distinguished from at least "K-1" other people in the same dataset. In other words, for any given record, there are at least K other records in the dataset with identical values for all identifying attributes.

In the literature, k-anonymity and differential privacy have been viewed as very different privacy guarantees. k- anonymity is syntactic and weak, and differential privacy is algorithmic and provides semantic privacy guarantees.

In an online context, we must consider three types of anonymity: sender anonymity, recipient anonymity and unlinkability of sender and recipient. The GDPR defines anonymous data as such that “does not relate to an identified or identifiable natural person“.

Therefore, by enforcing that data sets of a sensitive nature (such as medical or financial data sets) achieve k-anonymity with a high value of k, one can minimize the risk that an adversary will be able to uncover the identity of the person whose data is represented by a particular row.

A dataset is k-anonymous if quasi-identifiers for each person in the dataset are identical to at least k – 1 other people also in the dataset. You can compute the k-anonymity value based on one or more columns, or fields, of a dataset.

L Diversity enhances the inherent deficiencies in the K Anonymity model – notably eliminating the possibility of a hom*ogeneous pattern attack or a background knowledge attack – by introducing further entropy (or diversity) into a dataset. The result is significantly reduced risk of re-identification of anonymized data.

Anonymity portrays the researcher as a steward of knowledge, thus, it elevates the researcher to a position of power, no collaborative partnership or sharing of control. The participants are disenfranchised. It is paternalistic, it negates participants' autonomy and the right to make choices.

Online anonymity is often less helpful on a day-to-day basis and should be prioritized on a case-by-case basis. It's best to be anonymous anytime you're doing something you maybe shouldn't be doing or something you wouldn't want to be traced back to you or your profiles.

What problem does data anonymization protect against? ›

Advantages of data anonymization

These include: Enhanced data security: Anonymizing data can significantly reduce the risks associated with data breaches by removing or hiding sensitive and/or easily identifying details of personal information, such as names, addresses, and social security numbers.

One example of anonymized data is a dataset that has been stripped of any personally identifiable information such as names, addresses, and phone numbers. This type of data can be used to analyze trends and patterns without the risk of exposing any individual's personal information.

Information loss: In order to make individuals indistinguishable from one another, k-anonymity often requires the suppression or generalization of data. This can result in a significant loss of information and may make the data set less useful for certain types of analysis.

Anonymity means you don't know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations.

Anonymity – Keeping your identity private, but not your actions. For example, using a pseudonym to post messages to a social media platform.

K-anonymity is a property of a dataset that indicates the re-identifiability of its records. A dataset is k-anonymous if quasi-identifiers for each person in the dataset are identical to at least k – 1 other people also in the dataset.

• K-‐degree anonymity ensures that each node has the same degree. as at least k other nodes. – Limits reiden|fica|on by node degrees. • Algorithm: Generate a k-‐degree anonymous degree sequence, find a graph that is close to the original graph that has the new.

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.