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How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of∼k users, and its content-aware variants seek a set of∼k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select∼k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.

Display Omitted Patients re-identification may be performed based on their demographics and diagnosis codes.Existing approaches are unable to prevent the attack while preserving data utility.A new approach that is based on (k; km)-anonymity and utility constraints is proposed.Extensive experiments on healthcare data highlight the effectiveness of our approach. Publishing data about patients that contain both demographics and diagnosis codes is essential to perform large-scale, low-cost medical studies. However, preserving the privacy and utility of such data is challenging, because it requires: (i) guarding against identity disclosure (re-identification) attacks based on both demographics and diagnosis codes, (ii) ensuring that the anonymized data remain useful in intended analysis tasks, and (iii) minimizing the information loss, incurred by anonymization, to preserve the utility of general analysis tasks that are difficult to determine before data publishing. Existing anonymization approaches are not suitable for being used in this setting, because they cannot satisfy all three requirements. Therefore, in this work, we propose a new approach to deal with this problem. We enforce the requirement (i) by applying (k,km)-anonymity, a privacy principle that prevents re-identification from attackers who know the demographics of a patient and up to m of their diagnosis codes, where k and m are tunable parameters. To capture the requirement (ii), we propose the concept of utility constraint for both demographics and diagnosis codes. Utility constraints limit the amount of generalization and are specified by data owners (e.g., the healthcare institution that performs anonymization). We also capture requirement (iii), by employing well-established information loss measures for demographics and for diagnosis codes. To realize our approach, we develop an algorithm that enforces (k,km)-anonymity on a dataset containing both demographics and diagnosis codes, in a way that satisfies the specified utility constraints and with minimal information loss, according to the measures. Our experiments with a large dataset containing more than 200,000 electronic health records show the effectiveness and efficiency of our algorithm.

We envision a publish/subscribe ontology system that is able to index millions of user subscriptions and filter them against ontology data that arrive in a streaming fashion. In this work, we propose a SPARQL extension appropriate for a publish/subscribe setting; our extension builds on the natural semantic graph matching of the language and supports the creation of full-text subscriptions. Subsequently, we propose a main-memory subscription indexing algorithm which performs both semantic and full-text matching at low complexity and minimal filtering time. Thus, when ontology data are published matching subscriptions are identified and notifications are forwarded to users., Comment: ESWC 2012 Demo