A design strategy (the required ECC strength and the judgment method of the dominant error mode) of error-prediction low-density parity-check (EP-LDPC) error-correcting code (ECC) and error-recovery schemes for scaled NAND flash memories is discussed in this paper. The reliability characteristics of NAND flash memories are investigated with 1X, 2X and 3Xnm NAND flash memories. Moreover, the system-level reliability of SSDs is analyzed from the acceptable data-retention time of the SSD. The reliability of the NAND flash memory is continuously degrading as the design rule shrinks due to various problems. As a result, future SSDs will not be able to maintain system-level reliability unless advanced ECCs with signal processing are adopted. Therefore, EP-LDPC and error-recovery (ER) schemes are previously proposed to improve the reliability. The reliability characteristics such as the bit-error rate (BER) versus the data-retention time and the effect of the cell-to-cell interference on the BER are measured. These reliability characteristics obtained in this paper are stored in an SSD as a reliability table, which plays a principal role in EP-LDPC scheme. The effectiveness of the EP-LDPC scheme with the scaling of the NAND flash memory is also discussed by analyzing the cell-to-cell interference. An interference factor $alpha$ is proposed to discuss the impact of the cell-to-cell coupling. As a result, the EP-LDPC scheme is assumed to be effective down to 1Xnm NAND flash memory. On the other hand, the ER scheme applies different voltage pulses to memory cells, according to the dominant error mode: program-disturb or data-retention error dominant mode. This paper examines when the error mode changes, corresponding to which pulse should be applied. Additionally, the estimation methods of the dominant error mode by ER scheme are also discussed. Finally, as a result of the system-level reliability analysis, it is concluded that the use of the EP-LDPC scheme can maintain the reliability of the NAND flash memory in 1Xnm technology node.

This paper analyzes the correlation between various acoustic features and perceptual voice quality similarity, and proposes a perceptually similar speaker selection technique based on distance metric learning. To analyze the relationship between acoustic features and voice quality similarity, we first conduct a large-scale subjective experiment using the voices of 62 female speakers and perceptual voice quality similarity scores between all pairs of speakers are acquired. Next, multiple linear regression analysis is carried out; it shows that four acoustic features are highly correlated to voice quality similarity. The proposed speaker selection technique first trains a transform matrix based on distance metric learning using the perceptual voice quality similarity acquired in the subjective experiment. Given an input speech, acoustic features of the input speech are transformed using the trained transform matrix, after which speaker selection is performed based on the Euclidean distance on the transformed acoustic feature space. We perform speaker selection experiments and evaluate the performance of the proposed technique by comparing it to speaker selection without feature space transformation. The results indicate that transformation based on distance metric learning reduces the error rate by 53.9%.

Recently, a malicious user attacks a web browser through a malicious page that exploits the vulnerability of the browser and that executes malicious code. To prevent this attack, some methods have been devised such as DEP (Data Execution Prevention) that prevents data in stack frame or heap region from being executed. However, to evade these defense techniques, return-oriented programming (ROP) is introduced. ROP executes arbitrary code indirectly using gadget, which is group of instructions including ret instruction in a module that doesn't apply ASLR (Address Space Layout Randomization). In this paper, we propose a static approach to detect ROP payload in a network irrespective of the environment of the system under attack. Most studies have tried to detect ROP attacks using dynamic analysis, because ROP has various addresses of gadgets according to loaded modules. These methods have a limitation that must consider the environment of system to operate ROP, such as the version of OS and modules including gadgets. To overcome this limitation, our method detects ROP payload using static analysis without preliminary knowledge about the environment. We extract five characteristics of ROP and then propose a novel algorithm, STROP, to detect ROP in payload without execution. Our idea is as follows: STROP makes stack frame using input payload statically. It extracts addresses suspected as indicating gadgets and makes groups using the addresses. And then, STROP determine whether the payload includes ROP based on static characteristics. We implement a prototype using snort (network-based intrusion system) and evaluate it. Experiments show that our technique can detect ROP payload with a low number of false alarms. False positive (FP) is 1.3% for 2,239 benign files and 0.05-0.51% for 1GB packet dump file. Among 68 ROP payloads, STROP detects 51 payloads. This research can be applied to existing systems that collect malicious codes, such as Honeypot.

In this paper, the Quality of Experience (QoE) on Dynamic Adaptive Streaming based on HTTP (DASH) is researched. To study users' experience on DASH, extensive subjective tests are firstly designed and conducted, based on which, we research QoE enhancement in DASH and find that DASH ensures more fluent playback (less stall) than constant bitrate (CBR) streaming to promote users' satisfaction especially in mobile networks. Then we adopt two-way analysis of variance (ANOVA) tests in statistics to identify the effect of specific factors (segment bitrate, bitrate fluctuation pattern, and bitrate switching) that impair users' experience on DASH. The impairment functions are then derived for these influence factors based on the Primacy and Recency Effect, a psychological phenomenon that has been proved to exist in users' experience on DASH in this paper. And the final QoE evaluation model is proposed to provide high correlation assessment for QoE of DASH. The good performance of our QoE model is validated by the subjective tests. In addition, our QoE study on DASH is also applied for QoE management to propose a QoE-based bitrate adaptation strategy, which promotes users' experience on DASH more strongly than the strategy based on QoS.

We propose a novel phased array-fed dual-reflector antenna that reduces performance degradation caused by multiple reflection. The marked feature of the proposed configuration is that different reflector profiles are employed for the two orthogonal directions. The reflector profile in the beam-scanning section (vertical section) is set to an imaging reflector configuration, while the profile in the orthogonal non-beam-scanning section (horizontal section) is set to a ring-focus Cassegrain antenna configuration. In order to compare the proposed antenna with the conventional antenna in which multiple reflection was problematic, we designed a prototype antenna of the same size, and verified the validity of the proposed antenna. The results of the verification were that the gain in the designed central frequency increased by 0.4 dB, and the ripple of the gain frequency properties that was produced by multiple reflection was decreased by 1.1,dB. These results demonstrated the validity of the proposed antenna.

We considered the oxygen diffusivity around a conductive filament of resistive switching oxides, with the aim of designing material appropriate for highly reliable non-volatile memory. Both theoretical and experimental analyses were performed for this consideration. The theoretically obtained oxygen chemical potential difference, which works as a driving force for diffusion, significantly depends on a material. Then, we experimentally confirmed that the oxygen diffusion behaviors vary greatly depending on the chemical potential differences.

The concept of dual pairing vector spaces (DPVS) was introduced by Okamoto and Takashima in 2009, and it has been employed in various applications, functional encryption (FE) including attribute-based encryption (ABE) and inner-product encryption (IPE) as well as attribute-based signatures (ABS), generic conversion from composite-order group based schemes to prime-order group based ones and public-key watermarking. In this paper, we show the concept of DPVS, the major applications to FE and the key techniques employed in these applications. This paper presents them with placing more emphasis on plain and intuitive descriptions than formal preciseness.

We previously proposed an inaudible non-blind digital-audio watermarking approach based on cochlear delay (CD) characteristics. There are, however, three remaining issues with regard to blind-detectability, frame synchronization related to confidentiality, and reversibility. We attempted to solve these issues in developing the proposed approach by taking blind-detectability and reversibility of audio watermarking into consideration. Frame synchronization was also incorporated into the proposed approach to improve confidentiality. We evaluated inaudibility, robustness, and reversibility with the new approach by carrying out three objective tests (PEAQ, LSD, and bit-detection or SNR) and six robustness tests. The results revealed that inaudible, robust, blindly-detectable, and semi-reversible watermarking based on CD could be accomplished.

Password-protected secret sharing (PPSS, for short) schemes were proposed by Bagherzandi, Jarecki, Saxena and Lu. In this paper, we consider another attack for PPSS schemes which is based on public parameters and documents. We show that the protocol proposed by Bagherzandi et al. is broken with the attack. We then propose an enhanced protocol which is secure against the attack.

Secret sharing (SS) has been extensively studied as for both secure data storage and a fundamental building block for multiparty computation (MPC). Recently, Kikuchi et al. proposed a passively and unconditionally secure conversion protocol that converts from a share of a ramp scheme to another of homomorphic SS scheme. The share-size of the ramp scheme is small, and the homomorphic SS scheme is a class of SS schemes that includes Shamir's and replicated SS schemes, which are convenient for MPC. Therefore, their protocol is a conversion from an SS scheme whose share-size is small to MPC-friendly SS schemes, and can be applied to reduce the amount of data storage while maintaining extendibility to MPC. We propose five unconditionally and actively secure protocols in the honest majority. In this paper, we consider a privacy and correctness as security requirement and does not consider a robustness: A cheat caused by an active adversary must be detected. These protocols consist of two conversion protocols, two reveal protocols and a protocol generating specific randomness. Main protocols among them are two conversion protocols for bilateral conversion between a ramp scheme and linear SS scheme, and the others are building blocks of the main protocols. Linear SS scheme is a subset of homomorphic SS scheme but includes both Shamir's and replicated SS schemes. Therefore, these main protocols are conversions between an SS scheme whose share-size is small to MPC-friendly SS schemes. These main protocols are unconditionally and actively secure so if MPC protocols used after the conversion are actively secure, the whole system involving SS scheme, conversion, and MPC protocols can be unconditionally and actively secure by using our main protocols. One of our two main protocols is the first to convert from MPC-friendly SS schemes to the ramp scheme. This enhances applications, such as secure backup, of the conversion protocol. Other than the two main protocols, we propose a protocol for generating specific randomnesses and two reveal protocols as building blocks. The latter two reveal protocols are actively and unconditionally secure in the honest majority and requires O(n||F||)-bit communication per revealing, and we believe that it is independently interest.

While online social networking is a popular way for people to share information, it carries the risk of unintentionally disclosing personal information. One way to reduce this risk is to anonymize personal information in messages before they are posted. Furthermore, if personal information is somehow disclosed, the person who disclosed it should be identifiable. Several methods developed for anonymizing personal information in natural language text simply remove sensitive phrases, making the anonymized text message unnatural. Other methods change the message by using synonymization or structural alteration to create fingerprints for detecting disclosure, but they do not support the creation of a sufficient number of fingerprints for friends of an online social network user. We have developed a system for anonymizing personal information in text messages that generalizes sensitive phrases. It also creates a sufficient number of fingerprints of a message by using synonyms so that, if personal information is revealed online, the person who revealed it can be identified. A distribution metric is used to ensure that the degree of anonymization is appropriate for each group of friends. A threshold is used to improve the naturalness of the fingerprinted messages so that they do not catch the attention of attackers. Evaluation using about 55,000 personal tweets in English demonstrated that our system creates sufficiently natural fingerprinted messages for friends and groups of friends. The practicality of the system was demonstrated by creating a web application for controlling messages posted on Facebook.

In this paper, we posit that extension of SDN to support deeply and flexibly programmable, software-defined data plane significantly enhance SDN and NFV and their interaction in terms of (1) enhanced interaction between applications and networks, (2) optimization of network functions, and (3) rapid development of new network protocols. All of these benefits are expected to contribute to improving the quality of diversifying communication networks and services. We identify three major technical challenges for enabling software-defined data plane as (1) ease of programming, (2) reasonable and predictable performance and (3) isolation among multiple concurrent logics. We also promote application-driving thinking towards defining software defined data-plane. We briefly introduce our project FLARE and its related technologies and review four use cases of flexible and deeply programmable data plane.

In recent years, there has been growing interest in systems for sharing resources, which were originally used for personal purposes by individual users, among many unspecified users via a network. An example of such systems is a peer-to-peer (P2P) data storage system that enables users to share a portion of unused space in their own storage devices among themselves. In a recent paper on a P2P data storage system, the user behavior model was defined based on supply and demand functions that depend only on the storage space unit price in a virtual marketplace. However, it was implicitly assumed that other factors, such as unused space of storage devices possessed by users and additional storage space asked by users, did not affect the characteristics of the supply and demand functions. In addition, it was not clear how the values of parameters used in the user behavior model were determined. Therefore, in this paper, we modify the supply and demand functions and determine the values of their parameters by taking the above mentioned factors as well as the price structure of storage devices in a real marketplace into account. Moreover, we provide a numerical example to evaluate the social welfare realized by the P2P data storage system as a typical application of the modified supply and demand functions.

Our society has been getting more privacy-sensitive. Diverse information is given by users to information and communications technology (ICT) systems such as IC cards benefiting them. The information is stored as so-called big data, and there is concern over privacy violation. Visual information such as images and videos is also considered privacy-sensitive. The growing deployment of surveillance cameras and social network services has caused a privacy problem of information given from various sensors. To protect privacy of subjects presented in visual information, their face or figure is processed by means of pixelization or blurring. As image analysis technologies have made considerable progress, many attempts to automatically process flexible privacy protection have been made since 2000, and utilization of privacy information under some restrictions has been taken into account in recent years. This paper addresses the recent progress of privacy protection for visual information, showing our research projects: PriSurv, Digital Diorama (DD), and Mobile Privacy Protection (MPP). Furthermore, we discuss Harmonized Information Field (HIFI) for appropriate utilization of protected privacy information in a specific area.

This paper presents an efficient method for differential fault analysis (DFA) on substitution-permutation network (SPN)-based block ciphers. A combination of a permutation cancellation and an algebraic key filtering technique makes it possible to reduce the computational cost of key filtering significantly and therefore perform DFAs with new fault models injected at an earlier round, which defeats conventional countermeasures duplicating or recalculating the rounds of interest. In this paper, we apply the proposed DFA to the LED block cipher. Whereas existing DFAs employ fault models injected at the 30th round, the proposed DFA first employs a fault model injected at the 29th round. We demonstrate that the proposed DFA can obtain the key candidates with only one pair of correct and faulty ciphertexts in about 2.1h even from the 29th round fault model and the resulting key space is reduced to 24.04

With the rapid growth of high performance ICT (Information Communication Technologies) devices such as smart phones and tablet PCs, multitasking has become one of the popular ways of using mobile devices. The reasons users have adopted multitask operation are that it reduces the level of dissatisfaction regarding waiting time and makes effective use of time by switching their attention from the waiting process to other content. This is a good solution to the problem of waiting; however, it may cause another problem, which is the increase in traffic volume due to the multiple applications being worked on simultaneously. Thus, an effective method to control throughput adapted to the multitasking situation is required. This paper proposes a transmission rate control method for web browsing that takes multitasking behavior into account and quantitatively demonstrates the effect of service by two different field experiments. The main contribution of this paper is to present a service design process for a new transmission rate control that takes into account human-network interaction based on the human-centered approach. We show that the degree of satisfaction in relation to waiting time did not degrade even when a field trial using a testbed showed that throughput of the background task was reduced by 40%.

Counting the number of differentially active S-boxes is of great importance in evaluating the security of a block cipher against differential attack. Mouha et al. proposed a technique based on Mixed-Integer Linear Programming (MILP) to automatically calculate a lower bound of the number of differentially active S-boxes for word-oriented block ciphers, and applied it to symmetric ciphers AES and Enocoro-128v2. Later Sun et al. extended the method by introducing bit-level representations for S-boxes and new constraints in the MILP problem, and applied the extended method to PRESENT-80 and LBlock. This kind of methods greatly depends on the constraints in the MILP problem describing the differential propagation of the block cipher. A more accurate description of the differential propagation leads to a tighter bound on the number of differentially active S-boxes. In this paper, we refine the constraints in the MILP problem describing XOR operations, and apply the refined MILP modeling to determine a lower bound of the number of active S-boxes for the Lai-Massey type block cipher FOX in the model of single-key differential attack, and obtain a tighter bound in FOX64 than existing results. Experimental results show that 6, instead of currently known 8, rounds of FOX64 is strong enough to resist against basic single-key differential attack since the differential characteristic probability is upper bounded by 2-64, and thus the maximum differential characteristic probability of 12-round FOX64 is upper bounded by 2-128, where 128 is the key-length of FOX64. We also get the lower bound of the number of differentially active S-boxes for 5-round FOX128, and proved the security of the full-round FOX128 with respect to single-key differential attack.

The spatial relations between sensors placed for target detection can be inferred from the responses of individual sensors to the target objects. Motivated by this fact, this paper proposes a method for estimating the location of sensors by using their responses to target objects. The key idea of the proposal is that when two or more sensors simultaneously detect an object, the distances between these sensors are assumed to be equal to a constant called the basic range. Thus, new pieces of proximity information are obtained whenever an object passes over the area in which the sensors are deployed. This information is then be aggregated and transformed into a two dimensional map of sensors by solving a nonlinear optimization problem, where the value of the basic range is estimated together. Simulation experiments verify that the proposed algorithm yields accurate estimates of the locations of sensors.

We propose a new multi-label feature selection method that does not require the multi-label problem to be transformed into a single-label problem. Using quadratic programming, the proposed multi-label feature selection algorithm provides markedly better learning performance than conventional methods.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.