For expertise retrieval purposes we can rely on the endorsements received by members of a social network, with respect to some skill.Skills are correlated, which may affect individual rankings.An endorsement deduction method is proposed, which improves data consistency and completeness.Endorsement deduction makes use of the known correlation among skills.The method is validated in a synthetic network resembling LinkedIn. Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements. Display Omitted
Despite their unquestionable usefulness, systems based on document mining, like , face formidable challenges that limit their effectiveness. In addition to the specific challenges mentioned by @cite_15 , we could add several problems common to all data mining applications (e.g. name disambiguation). As a small experiment, we have searched for some known names in , and we get several profiles corresponding to the same person, one for each different spelling.
@cite_15
2111272198 2798768357 2047370889 2083042205
Despite its benefit in a wide range of applications, data mining techniques also have raised a number of ethical issues. Some such issues include those of privacy, data security, intellectual property rights, and many others. In this paper, we address the privacy problem against unauthorized secondary use of information. To do so, we introduce a family of geometric data transformation methods (GDTMs) which ensure that the mining process will not violate privacy up to a certain degree of security. We focus primarily on privacy preserving data clustering, notably on partition-based and hierarchical methods. Our proposed methods distort only confidential numerical attributes to meet privacy requirements, while preserving general features for clustering analysis. Our experiments demonstrate that our methods are effective and provide acceptable values in practice for balancing privacy and accuracy. We report the main results of our performance evaluation and discuss some open research issues. Text mining and information retrieval techniques have been developed to assist us with analyzing, organizing and retrieving documents with the help of computers. In many cases, it is desirable that the authors of such documents remain anonymous: Search logs can reveal sensitive details about a user, critical articles or messages about a company or government might have severe or fatal consequences for a critic, and negative feedback in customer surveys might negatively impact business relations if they are identified. Simply removing personally identifying information from a document is, however, insufficient to protect the writer's identity: Given some reference texts of suspect authors, so-called authorship attribution methods can reidentfy the author from the text itself. One of the most prominent models to represent documents in many common text mining and information retrieval tasks is the vector space model where each document is represented as a vector, typically containing its term frequencies or related quantities. We therefore propose an automated text anonymization approach that produces synthetic term frequency vectors for the input documents that can be used in lieu of the original vectors. We evaluate our method on an exemplary text classification task and demonstrate that it only has a low impact on its accuracy. In contrast, we show that our method strongly affects authorship attribution techniques to the level that they become infeasible with a much stronger decline in accuracy. Other than previous authorship obfuscation methods, our approach is the first that fulfills differential privacy and hence comes with a provable plausible deniability guarantee. In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated by the need both to protect privileged information and to enable its use for research or other purposes. The above problem is a specific example of secure multi-party computation and, as such, can be solved using known generic protocols. However, data mining algorithms are typically complex and, furthermore, the input usually consists of massive data sets. The generic protocols in such a case are of no practical use and therefore more efficient protocols are required. We focus on the problem of decision tree learning with the popular ID3 algorithm. Our protocol is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth. Protection of privacy has become an important problem in data mining. In particular, individuals have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. Random perturbation and randomized response techniques can provide some level of privacy in data collection, but they have an associated cost in accuracy. Cryptographic privacy-preserving data mining methods provide good privacy and accuracy properties. However, in order to be efficient, those solutions must be tailored to specific mining tasks, thereby losing generality.In this paper, we propose efficient cryptographic techniques for online data collection in which data from a large number of respondents is collected anonymously, without the help of a trusted third party. That is, our solution allows the miner to collect the original data from each respondent, but in such a way that the miner cannot link a respondent's data to the respondent. An advantage of such a solution is that, because it does not change the actual data, its success does not depend on the underlying data mining problem. We provide proofs of the correctness and privacy of our solution, as well as experimental data that demonstrates its efficiency. We also extend our solution to tolerate certain kinds of malicious behavior of the participants.