Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. In particular, we focus on the setting in which we are given histograms of users’ data collected during two different experiments. We assume that, in the first data set, the users’ identities are anonymized or hidden and that, in the second data set, their identities are known. We study the task of identifying the users by matching the histograms of their data in the first data set with the histograms from the second data set. In recent works, the optimal algorithm for this user identification task is introduced. In this paper, we evaluate the effectiveness of this method on three different types of data sets with up to 50 000 users, and in multiple scenarios. Using data sets such as call data records, web browsing histories, and GPS trajectories, we demonstrate that a large fraction of users can be easily identified given only histograms of their data; hence, these histograms can act as users’ fingerprints. We also verify that simultaneous identification of users achieves better performance compared with one-by-one user identification. Furthermore, we show that using the optimal method for identification indeed gives higher identification accuracy than the heuristics-based approaches in the practical scenarios. The accuracy obtained under this optimal method can thus be used to quantify the maximum level of user identification that is possible in such settings. We show that the key factors affecting the accuracy of the optimal identification algorithm are the duration of the data collection, the number of users in the anonymized data set, and the resolution of the data set. We also analyze the effectiveness of @math -anonymization in resisting user identification attacks on these data sets.
A matching problem studied in the database community is that of identifying different data records that refer to the same real-world object @cite_40 . Similarly, in natural-language processing, the problem of linking different mentions of the same underlying entity in text @cite_16 is analogous to the objective in the user-matching problem. Another example from the information-retrieval literature is the problem of classifying documents by their authors, given documents from different authors with the same name @cite_34 . User matching has also been studied in the social-networks community in which the objective is to identify different profiles that belong to the same underlying user @cite_33 . Such problems fall under the umbrella-term @cite_38 . In these problems, the available information about the users is often not in the form of histograms, and the solutions proposed are often based on heuristics and practical convenience; whereas the solution we propose in this paper is specific to the setting in which the only information available about the users is in the form of histograms, and in this setting, the solution is optimal for minimizing the probability of misclassification.
@cite_38 @cite_33 @cite_34 @cite_40 @cite_16
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We are given a collection D of text documents d 1 ,…,d k , with ∑ i = n, which may be preprocessed. In the document listing problem, we are given an online query comprising of a pattern string p of length m and our goal is to return the set of all documents that contain one or more copies of p. In the closely related occurrence listing problem, we output the set of all positions within the documents where pattern p occurs. In 1973, Weiner [24] presented an algorithm with O(n) time and space preprocessing following which the occurrence listing problem can be solved in time O(m + output) where output is the number of positions where p occurs; this algorithm is clearly optimal. In contrast, no optimal algorithm is known for the closely related document listing problem, which is perhaps more natural and certainly well-motivated.We provide the first known optimal algorithm for the document listing problem. More generally, we initiate the study of pattern matching problems that require retrieving documents matched by the patterns; this contrasts with pattern matching problems that have been studied more frequently, namely, those that involve retrieving all occurrences of patterns. We consider document retrieval problems that are motivated by online query processing in databases, Information Retrieval systems and Computational Biology. We present very efficient (optimal) algorithms for our document retrieval problems. Our approach for solving such problems involve performing "local" encodings whereby they are reduced to range query problems on geometric objects --- points and lines --- that have color. We present improved algorithms for these colored range query problems that arise in our reductions using the structural properties of strings. This approach is quite general and yields simple, efficient, implementable algorithms for all the document retrieval problems in this paper. This paper introduces the problem of matching people names to their corresponding social network identities such as their Twitter accounts. Existing tools for this purpose build upon naive textual matching and inevitably suffer low precision, due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage "relational" evidences extracted from the Web corpus. In particular, as such an example, weadopt Web document co-occurrences, which can be interpreted as an "implicit" counterpart of Twitter follower relationships. Using both textual and relational features, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate to outperform the baseline approach. We evaluate our proposed system using real-life internetscale entity-relationship and social network graphs. The nearestor near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the of dimensionality." That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points Supported by NAVY N00014-96-1-1221 grant and NSF Grant IIS-9811904. Supported by Stanford Graduate Fellowship and NSF NYI Award CCR-9357849. Supported by ARO MURI Grant DAAH04-96-1-0007, NSF Grant IIS-9811904, and NSF Young Investigator Award CCR9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions (more than 50). Given an entity in a source domain, finding its matched entities from another (target) domain is an important task in many applications. Traditionally, the problem was usually addressed by first extracting major keywords corresponding to the source entity and then query relevant entities from the target domain using those keywords. However, the method would inevitably fails if the two domains have less or no overlapping in the content. An extreme case is that the source domain is in English and the target domain is in Chinese. In this paper, we formalize the problem as entity matching across heterogeneous sources and propose a probabilistic topic model to solve the problem. The model integrates the topic extraction and entity matching, two core subtasks for dealing with the problem, into a unified model. Specifically, for handling the text disjointing problem, we use a cross-sampling process in our model to extract topics with terms coming from all the sources, and leverage existing matching relations through latent topic layers instead of at text layers. Benefit from the proposed model, we can not only find the matched documents for a query entity, but also explain why these documents are related by showing the common topics they share. Our experiments in two real-world applications show that the proposed model can extensively improve the matching performance (+19.8 and +7.1 in two applications respectively) compared with several alternative methods.