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The original paper on k -anonymity defined k-anonymity: A table provides k -anon

ID: 3685531 • Letter: T

Question

The original paper on k -anonymity defined k-anonymity:

A table provides k -anonymity if attempts to link explicitly identifying information to its contents ambiguously map the information to at least k entities.
A later paper defined k-anonymity slightly differently: Each release of data must be such that every combination of values of quasi-identifiers can be indistinctly matched to at least k individuals.

1) Are these definitions the same? If the same, prove it (that is, show definition 1 implies definition

2 and vice versa). If different, give an example of a table/database that satisfies one definition but not the other.

Explanation / Answer

The original paper on k -anonymity defined k-anonymity: A table provides k -anonymity if attempts to link explicitly identifying information to its contents ambiguously map the information to at least k entities. A later paper defined k-anonymity slightly differently: Each release of data must be such that every combination of values of quasi-identifiers can be indistinctly matched to at least k individuals.

1) These definitions the same

K – Anonymity approach: each release of data must be such that every combination of values of quasi-identifiers can be indistinctly matched to at least K responds.

As each respondent is assumed to be represented by at most one tuple in the table and vice versa.

(Each tuble includes information related to one respondent only), a microdata table satisfies the k-anonymity requirement if and only if:

1. Each tuple in the released table cannot be related to less K individuals in the population.

2. Each individual in the population cannot be related to less than K tuples in the table.

Where some of the quasi identifier fields are suppressed or generalized. A table satisfies k anonymity if every record in the table is indistinguishable from at least k -1 other record with respect to every set of quasi-identifier attributes. Such a table is called a k anonymous table.

For Example:

A key step in extracting the semantics of Web content is entity linking (EL): the task of mapping a phrase in text to its referent entity in a knowledge base (KB).

We present TabEL , a new EL system for Web tables.

The assumption that the semantics of a table can be mapped to pre-defined types and relations found in the target KB. Instead, TabEL enforces soft constraints in the form of a graphical model that assigns higher likelihood to sets of entities that tend to co-occur in Wikipedia documents and tables.

We present TabEL, a system that performs the Entity Linking task on phrases in cells of Web tables. Existing table semantic interpretation systems typically employ graphical models to jointly model three semantic interpretation tasks: entity linking, column type identification and relation extraction from tables

1. Entity linking (EL): the task of finding phrases of text, called mentions, in cells and associating each with its referent entity

2. Column type identification: the task of associating a column in a table with the KB type of entities it contains

3. Relation extraction: the task of associating a pair of columns in a table with the KB relation that holds between each pair of entities in a given row of the columns

Given a table T and a KB K of entities, the entity linking task is to identify and link each potential mention in cells of T to its referent entity e 2 K.

Given a table T and a KB K, TabEL performs the EL task in three steps:

1. Mention identification: identifies each potential mention, in cells of T

2. Entity candidate generation: for each potential mention, identifies a set of candidate entities, C- a subset of entities in K that are possible referents.

3. Disambiguation: for each potential mention ms, as the referent entity of ms, based on its context.

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