Creatividad en el Spot

A search service provider wants to release the collection T of vocabularies

extracted from a query log Q. As the number of occurrences of a query-term is important information, the provider treats the vocabularies as bags instead of sets. The provider considers a spectrum of granularities in merging queries into vocabularies since there is no one-size-fits-all setting of the granularity, i.e., a different application may require a different granularity level. E.g., web search personalization may require a granularity at the user level, while query suggestion probably needs a granularity at the session level.

One way to specify the granularity, i.e., to delimit the logical vocabularies of users, is by the gap between the times that queries were posted, which is an approach also documented in web mining literature. Given a query log Q in the format as shown in Figure 5.1(a) where for q in Q, q.AnonID is the anonymous ID of the user issuing q, q.Query is the set of query-terms in q, and q.QueryTime is the time that q was posted, vocabularies can be extracted based on an application dependent logical session gap, sGap, as follows.

Figure 5.1 Vocabularies extracted from a query log

Definition 5.1 (Collection T of vocabularies): Given a logical session gap sGap

and Q = {q1, q2, … qn} with queries in ascending order of AnonID and QueryTime, queries in Q are distributed into groups Gj. Suppose that a query qi is distributed to Gj. The next query qi+1 is distributed to Gj+1 if qi.AnonID < qi+1.AnonID or qi.QueryTime +

AnonID Query QueryTime ItemRank ClickURL

01 Wine 1:00Jan1 3 a.c

01 Wine 1:00Jan1 8 b.c

01 Jackets,Boots 1:15Jan1 3 c.c

01 Jackets,Boots 1:15Jan1 5 d.c

01 Wine 2:05Jan1 4 x.c

02 _{··· ··· ··· ···}

(a) Q: original web query log

# Vocabulary of each user

t1 Wine, Wine, Jackets, Boots

t2 Vino, Vino, Jackets, Shoes

t3 Wine, Vino, Raw-milk, Jackets, Shoes

t4 Vino, Raw-milk, Raw-milk, Homo-milk

t5 Raw-milk,Homo-milk,Homo-milk,Jackets,Pants

sGap < qi+1.QueryTime, otherwise qi+1 is also distributed to Gj. The bag containing all the terms in q.Query for q ∈ Gj is the jth vocabulary in T = Vocabulary(Q, sGap). ■

One additional option in extracting vocabularies is to leave out the queries in which the search users are not interested. That is, for q in Q if q.ItemRank and q.ClickURL are empty, i.e., no search result of q is clicked, we can leave out q.

Example 5.2: Figure 5.1(a) shows 3 queries, the first query about {Wine} has two

clicked search results, i.e., the first 2 lines with ClickURL "a.c" and "b.c", and so on. Figure 5.1(b) shows the vocabularies extracted with sGap=∞ (one vocabulary per user). ■

The search service provider wants to protect the identities of search service users by observing the following privacy principle.

Definition 5.2 (Vocabulary k-anonymity): Given a query log Q and a logical

session gap sGap, the vocabulary k-anonymity principle is observed if for any vocabulary t ∈ T = Vocabulary(Q, sGap), there are at least k-1 other vocabularies in T that are indistinguishable from t. ■

The assumption is that an adversary has knowledge about how often his/her target individual using any combination of certain query-terms. This privacy principle aims at preventing such an adversary from linking any vocabulary to his/her target individual with a probability greater than 1 / k.

5.1.2 Anonymizing Vocabularies by Semantic Similarity Based Clustering

This chapter proposes the semantic similarity based clustering to achieve the vocabulary k-anonymity as follows. T = Vocabulary(Q, sGap) is partitioned into a set of clusters by the semantic similarities such that each cluster contains at least k vocabularies.

The centre of each cluster is selected as the typical vocabulary to represent all the vocabularies in the cluster. In clustering vocabularies and selecting the typical

vocabularies, we allow setting a semantic distance threshold ε to control the similarity degree that is required for vocabularies to comprise a cluster and for one typical query- term to represent others.

# Vocabulary Our approach

t1 W, W, J, B W, V, J, S

t2 V, V, J, S W, V, J, S

t3 W, V, R, J, S W, V, J, S

t4 V, R, R, H R, R, H

t5 R, H, H, J, P R, R, H

Figure 5.2 Anonymizing vocabularies by semantic similarity based clustering

Example 5.3 (A running example): Consider enforcing vocabulary 2-anonymity

on the collection T of vocabularies in Figure 5.1(b). To comparing with generalization techniques, the taxonomy tree in Figure 5.2(a) is always used to measure the semantic relationship between query-terms. We partition the five vocabularies into two clusters. The first cluster consists of the first three vocabularies, and the second cluster is made of the last two vocabularies. The cluster centre, i.e., the typical vocabulary, of each cluster is elected on a majority vote basis, that is {Wine:1, Vino:1, Jackets:1, Shoes:1} for the first cluster, and {Raw-Milk:2, Homo-Milk:1} for the second cluster. The last column in Figure 5.2(b) shows the anonymized vocabularies by our approach. ■

The proposed approach can retain more data utility than conventional approaches for the following reasons.

First, instead of generalizing query-terms, we substitute query-terms by

semantically similar, original query-terms. One may argue that generalizing Wine and Vino into Alcohol can keep more truth than distorting Wine into Vino. However, because of the extreme sparsity, Wine and Vino will be generalized into Edibles or even Goods, instead of Alcohol, which conveys little information. More discussion is presented in Section 5.5.1.

Second, the query-terms that do not match with the others in a cluster will be left out from the cluster, i.e., local suppression is an inherent component of our approach. As an alternative, we can also determine the typical vocabulary of a cluster by locally generalizing semantically matched terms in the cluster. With this alternative, our approach becomes the integration of local suppression and local generalization, which implies that our approach takes advantage of both the generalization and suppression techniques.