A true random number generator (TRNG) is proposed and evaluated by field-programmable gate arrays (FPGA) implementation that generates random numbers by exclusive-ORing (XORing) the outputs of many SR latches (Hata and Ichikawa, IEICE Trans. Inf. Syst. E95-D(2):426–436, 2012). This enables compact implementation and generates high-entropy random numbers. In this paper, we fabricate and evaluate 39 TRNGs using SR latches on 0.18 μ m ASICs. Random numbers are generated by XORing the outputs of 256 SR latches. Our TRNGs pass the SP800-90B health tests and the AIS20/31 statistical tests in changing temperatures (from −20 to 60 °C) and voltages (1.80 ± 0.15 V). We also perform an independent and identically distributed (IID) test and calculate min-entropy according to the SP800-90B. With these tests, we are able to confirm that our TRNGs are highly robust against environmental stress. The power consumption and circuit scale of our TRNGs are 0.27 mW and 1240.5 gates, respectively. Our TRNGs that use SR latches are small enough to be implemented in embedded devices.

This paper studies a privacy-preserving decision tree learning protocol (PPDT) for vertically partitioned datasets. In the vertically partitioned datasets, a single class (target) attribute are shared by both parities or carefully treated by either party in the existing studies. The proposed scheme allows both parties to have independent class attributes in secure way and to combine multiple class attributes in arbitrary boolean function, which gives parties a flexibility in data-mining. Our proposed PPDT protocol reduces the CPU intensive computation of logarithm by approximating with the piecewise linear function defined by light-weight fundamental operations of addition and constant-multiplication so that information gain for attribute can be evaluated in the secure function evaluation scheme. Using the UCI Machine Learning dataset and the synthesized dataset, the proposed protocol is evaluated in terms of the accuracy and the size of tree.

The Guest Editors' primary objective in organizing this Special Issue was to provide additional impetus for research in hardware security. Currently, the field is heavily dominated by testing, CAD, and IC researchers. They hope that researchers from other security fields will find the problems and the proposed solutions published here both interesting and important. Ten Special Issue papers are represented in this collection.

This paper proposes a novel method for the automated detection of breast tumors in three dimensional ultrasonic images using fuzzy reasoning. The algorithm consists of two stages of processing. In the first stage a three dimensional LoG (Laplace of Gaussian) filtering is employed to generate a membership function for the fuzzy inference. In the second stage, each voxel in the three dimensional image is classified as "tumor", "normal tissue" or "boundary" by fuzzy reasoning followed by the defuzzification using relaxation techniques. We apply the method to 10 cases of malignant and 10 cases of benign tumors. Both of the two types of extracted tumors agree well with the manually traced boundaries. Further we define a parameter for evaluation of the uneven complexity of the surface roughness for diagnosis of malignant and benign tumors. The results show a successful characterization.