Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC.

Privileged Multi-label Learning

This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performance on examples can receive the explicit comments from other labels together acting as an Oracle teacher. We generate privileged label feature for each example and its individual label, and then integrate it into the framework of low-rank based multi-label learning. The proposed algorithm can therefore comprehensively explore and exploit label relationships by inheriting all the merits of privileged information and low-rank constraints. We show that PrML can be efficiently solved by dual coordinate descent algorithm using iterative optimization strategy with cheap updates. Experiments on benchmark datasets show that through privileged label features, the performance can be significantly improved and PrML is superior to several competing methods in most cases.