Ke Wang

TL;DR: This survey will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish P PDP from other related problems, and propose future research directions.

TL;DR: A Convolutional Sequence Embedding Recommendation Model »Caser» is proposed, which is to embed a sequence of recent items into an image in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters.

TL;DR: It is proved that the optimal (α, k)-anonymity problem is NP-hard, and a local-recoding algorithm is proposed which is more scalable and result in less data distortion.

TL;DR: The results show that quality of classification can be preserved even for highly restrictive privacy requirements, and has great applicability to both public and private sectors that share information for mutual benefits and productivity.

TL;DR: PSORT-B, an updated version of PSORT for Gram-negative bacteria, is presented, designed to favor high precision over high recall (sensitivity), and attained an overall precision of 97% and recall of 75% in 5-fold cross-validation tests, using a dataset the authors developed of 1443 proteins of experimentally known localization.