Sentence examples for distinguish queries from inspiring English sources

Once the leaf nodes for all the cover searches are received, the nodes of each stage are shuffled by the client and written back to the B-Tree at the SP. Thus, the access patterns of two identical queries look different to the SP. However, since only e nodes are shuffled, the SP is still able to distinguish queries with a certain probability.

In both cases, this attack is based on two steps: it first builds and trains a model for each user from its query history (and for TMN, it builds and trains a model from fake queries), and then it leverages these models to de-anonymize anonymous queries or to distinguish fake queries from real ones.

We then analyze the performance of SimAttack when the adversary leverages prior knowledge on the RSS feeds of the users in order to distinguish fake queries to real queries.

We now analyze the robustness of TMN when SimAttack uses prior knowledge about the RSS feeds of the users to distinguish fake queries from the users' queries (as explained in Section 4.3).

In this section, we evaluate the capacity of SimAttack to distinguish fake queries sent by TrackMeNot from the real queries sent by users.

Natural language processing can distinguish between queries such as "rules for innovation" and "rules by innovation".

Then, the adversary uses this external knowledge to distinguish fake queries (line 10).

In this section, we evaluate the capacity of SimAttack to distinguish fake queries generated by GooPIR.

However, as these RSS feeds are set up by default or manually by the user, an adversary could be able to distinguish real queries from fake ones.

To distinguish fake queries from real ones, the concurrent attack considers several machine learning algorithms: Logistic Regression, Alternating Decision Trees, Random Forest, Random Tree and ZeroR.

It was also able to distinguish long queries such as "translate to Spanish 'Where can I buy a hamburger in this neighborhood?'".