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.

Application of Lattice-Gas Model to the Simulation of Complex Economical Systems .SOIJST Vol. 1 (1):59-72

The lattice-gas model simulations are being used to reproduce the collective behavior of complex economic systems. By correlating different manifestation of the collective behaviors to the parameter changes in the simulation, we identify the underlying mechanism of the various manifestation of the collective behavior. The standard growth model is reproduced by the model with non-interacting agent. As we introduce interacting agents, the growth model departs from the standard. Using cluster labeling algorithm we obtain number density histograms and diversity to explain the underlying mechanism of the growth dynamics.