Reversible Data Perturbation Techniques for Multi-level Privacy-preserving Data Publication

The amount of digital data generated in the Big Data age is increasingly rapidly. Privacy-preserving data publishing techniques based on differential privacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption that all users of the data share the same level of privilege and access the data with a fixed privacy level. Thus, such schemes do not directly support data release in cases when data users have different levels of access on the published data. While a straight-forward approach of releasing a separate snapshot of the data for each possible data access level can allow multi-level access, it can result in a higher storage cost requiring separate storage space for each instance of the published data. In this paper, we develop a set of reversible data perturbation techniques for large bipartite association graphs that use perturbation keys to control the sequential generation of multiple snapshots of the data to offer multi-level access based on privacy levels. The proposed schemes enable multi-level data privacy, allowing selective de-perturbation of the published data when suitable access credentials are provided. We evaluate the techniques through extensive experiments on a large real-world association graph dataset and our experiments show that the proposed techniques are efficient, scalable and effectively support multi-level data privacy on the published data

Reproducibility of experiments in recommender systems evaluation

© IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved. Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning recommendations of higher quality. However, the comparison of results is becoming increasingly difficult, since there are different recommendation frameworks and different settings in the design and implementation of the experiments. Furthermore, there might be minor differences on algorithm implementation among the different frameworks. In this paper, we compare well known recommendation algorithms, using the same dataset, metrics and overall settings, the results of which point to result differences across frameworks with the exact same settings. Hence, we propose the use of standards that should be followed as guidelines to ensure the replication of experiments and the reproducibility of the results

Local Differentially Private Matrix Factorization with MoG for Recommendations

Unethical data aggregation practices of many recommendation systems have raised privacy concerns among users. Local differential privacy (LDP) based recommendation systems address this problem by perturbing a user’s original data locally in their device before sending it to the data aggregator (DA). The DA performs recommendations over perturbed data which causes substantial prediction error. To tackle privacy and utility issues with untrustworthy DA in recommendation systems, we propose a novel LDP matrix factorization (MF) with mixture of Gaussian (MoG). We use a Bounded Laplace mechanism (BLP) to perturb user’s original ratings locally. BLP restricts the perturbed ratings to a predefined output domain, thus reducing the level of noise aggregated at DA. The MoG method estimates the noise added to the original ratings, which further improves the prediction accuracy without violating the principles of differential privacy (DP). With Movielens and Jester datasets, we demonstrate that our method offers a higher prediction accuracy under strong privacy protection compared to existing LDP recommendation methods

WTEN: An advanced coupled tensor factorization strategy for learning from imbalanced data

© Springer International Publishing AG 2016. Learning from imbalanced and sparse data in multi-mode and high-dimensional tensor formats efficiently is a significant problem in data mining research. On one hand,Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of heterogeneous sparse data generated from different sources. On the other hand,techniques such as sampling,cost-sensitive learning,etc. have been applied to many supervised learning models to handle imbalanced data. This research focuses on studying the effectiveness of combining advantages of both CTF and imbalanced data learning techniques for missing entry prediction,especially for entries with rare class labels. Importantly,we have also investigated the implication of joint analysis of the main tensor and extra information. One of our major goals is to design a robust weighting strategy for CTF to be able to not only effectively recover missing entries but also perform well when the entries are associated with imbalanced labels. Experiments on both real and synthetic datasets show that our approach outperforms existing CTF algorithms on imbalanced data

Does mass drug administration for the integrated treatment of neglected tropical diseases really work? Assessing evidence for the control of schistosomiasis and soil-transmitted helminths in Uganda

This paper was one of four papers commissioned to review the role of social sciences in NTD control by TDR, the Special Programme for Research and Training on Tropical Diseases, which is executed by WHO and co-sponsored by UNICEF, UNDP, the World Bank and WHO.This article has been made available through the Brunel Open Access Publishing Fund.Background: Less is known about mass drug administration [MDA] for neglected tropical diseases [NTDs] than is suggested by those so vigorously promoting expansion of the approach. This paper fills an important gap: it draws upon local level research to examine the roll out of treatment for two NTDs, schistosomiasis and soil-transmitted helminths, in Uganda. Methods: Ethnographic research was undertaken over a period of four years between 2005-2009 in north-west and south-east Uganda. In addition to participant observation, survey data recording self-reported take-up of drugs for schistosomiasis, soil-transmitted helminths and, where relevant, lymphatic filariasis and onchocerciasis was collected from a random sample of at least 10% of households at study locations. Data recording the take-up of drugs in Ministry of Health registers for NTDs were analysed in the light of these ethnographic and social survey data. Results: The comparative analysis of the take-up of drugs among adults revealed that although most long term residents have been offered treatment at least once since 2004, the actual take up of drugs for schistosomiasis and soil-transmitted helminths varies considerably from one district to another and often also within districts. The specific reasons why MDA succeeds in some locations and falters in others relates to local dynamics. Issues such as population movement across borders, changing food supply, relations between drug distributors and targeted groups, rumours and conspiracy theories about the 'real' purpose of treatment, subjective experiences of side effects from treatment, alternative understandings of affliction, responses to social control measures and historical experiences of public health control measures, can all make a huge difference. The paper highlights the need to adapt MDA to local circumstances. It also points to specific generalisable issues, notably with respect to health education, drug distribution and more effective use of existing public health legislation. Conclusion: While it has been an achievement to have offered free drugs to so many adults, current standard practices of monitoring, evaluation and delivery of MDA for NTDs are inconsistent and inadequate. Efforts to integrate programmes have exacerbated the difficulties. Improved assessment of what is really happening on the ground will be an essential step in achieving long-term overall reduction of the NTD burden for impoverished communities.This article is available through the Brunel Open Access Publishing Fund

How to combine visual features with tags to improve movie recommendation accuracy?

Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However, the stylistic visual features can be also used when other sources of information is available (Existing Item scenario). In this work, we address the second scenario and propose a hybrid technique that exploits not only the typical content available for the movies (e.g., tags), but also the stylistic visual content extracted form the movie files and fuse them by applying a fusion method called Canonical Correlation Analysis (CCA). Our experiments on a large catalogue of 13K movies have shown very promising results which indicates a considerable improvement of the recommendation quality by using a proper fusion of the stylistic visual features with other type of features

The assessment of neuromuscular fatigue during 120 min of simulated soccer exercise

Purpose This investigation examined the development of neuromuscular fatigue during a simulated soccer match incorporating a period of extra time (ET) and the reliability of these responses on repeated test occasions. Methods Ten male amateur football players completed a 120 min soccer match simulation (SMS). Before, at half time (HT), full time (FT), and following a period of ET, twitch responses to supramaximal femoral nerve and transcranial magnetic stimulation (TMS) were obtained from the knee-extensors to measure neuromuscular fatigue. Within 7 days of the first SMS, a second 120 min SMS was performed by eight of the original ten participants to assess the reliability of the fatigue response. Results At HT, FT, and ET, reductions in maximal voluntary force (MVC; −11, −20 and −27%, respectively, P ≤ 0.01), potentiated twitch force (−15, −23 and −23%, respectively, P < 0.05), voluntary activation (FT, −15 and ET, −18%, P ≤ 0.01), and voluntary activation measured with TMS (−11, −15 and −17%, respectively, P ≤ 0.01) were evident. The fatigue response was robust across both trials; the change in MVC at each time point demonstrated a good level of reliability (CV range 6–11%; ICC2,1 0.83–0.94), whilst the responses identified with motor nerve stimulation showed a moderate level of reliability (CV range 5–18%; ICC2,1 0.63–0.89) and the data obtained with motor cortex stimulation showed an excellent level of reliability (CV range 3–6%; ICC2,1 0.90–0.98). Conclusion Simulated soccer exercise induces a significant level of fatigue, which is consistent on repeat tests, and involves both central and peripheral mechanisms