In this paper, we propose a selective membership inference attack method that determines whether certain data corresponding to a specific class are being used as training data for a machine learning model or not. By using the proposed method, membership or non-membership can be inferred by generating a decision model from the prediction of the inference models and training the confidence values for the data corresponding to the selected class. We used MNIST as an experimental dataset and Tensorflow as a machine learning library. Experimental results show that the proposed method has a 92.4% success rate with 5 inference models for data corresponding to a specific class.

The membership check of a group is an important operation to implement discrete logarithm-based cryptography in practice securely. Since this check requires costly scalar multiplication or exponentiation operation, several efficient methods have been investigated. In the case of pairing-based cryptography, this is an extended research area of discrete logarithm-based cryptography, Barreto et al. (LATINCRYPT 2015) proposed a parameter choice called subgroup-secure elliptic curves. They also claimed that, in some schemes, if an elliptic curve is subgroup-secure, costly scalar multiplication or exponentiation operation can be omitted from the membership check of bilinear groups, which results in faster schemes than the original ones. They also noticed that some schemes would not maintain security with this omission. However, they did not show the explicit condition of what schemes become insecure with the omission. In this paper, we show a concrete example of insecurity in the sense of subgroup security to help developers understand what subgroup security is and what properties are preserved. In our conclusion, we recommend that the developers use the original membership check because it is a general and straightforward method to implement schemes securely. If the developers want to use the subgroup-secure elliptic curves and to omit the costly operation in a scheme for performance reasons, it is critical to carefully analyze again that correctness and security are preserved with the omission.

Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.

A regular pattern is a string consisting of constant symbols and distinct variable symbols. The language of a regular pattern is the set of all constant strings obtained by replacing all variable symbols in the regular pattern with non-empty strings. The present paper deals with the learning problem of languages of regular patterns within Angluin's query learning model, which is an established mathematical model of learning via queries in computational learning theory. The class of languages of regular patterns was known to be identifiable from one positive example using a polynomial number of membership queries, in the query learning model. In present paper, we show that the class of languages of regular patterns is identifiable from one positive example using a linear number of membership queries, with respect to the length of the positive example.

Advanced metering infrastructure (AMI) is a kind of wireless sensor network that provides two-way communication between smart meters and city utilities in the neighborhood area of the smart grid. And the routing protocol for low-power and lossy network (RPL) is being considered for use in AMI networks. However, there still exist several problems that need to be solved, especially with respect to QoS guarantees. To address these problems, an improved algorithm of RPL based on triangle module operator named as TMO is proposed. TMO comprehensively evaluates routing metrics: end-to-end delay, number of hops, expected transmission count, node remaining energy, and child node count. Moreover, TMO uses triangle module operator to fuse membership functions of these routing metrics. Then, the node with minimum rank value will be selected as preferred parent (the next hop). Consequently, the QoS of RPL-based AMI networks can be guaranteed effectively. Simulation results show that TMO offers a great improvement over several the most popular schemes for RPL like ETXOF, OF-FL and additive composition metric manners in terms of network lifetime, average end-to-end delay, average packet loss ratio, average hop count from nodes to root, etc.

Subspace membership encryption (SME), a generalization of inner product encryption (IPE), was recently formalized by Boneh, Raghunathan, and Segev in Asiacrypt 2013. The main motivation for SME was that traditional predicate encryptions did not yield function privacy, a security notion introduced by Boneh et al. in Crypto 2013 that captures the privacy of the predicate associated to the secret key. Although they gave a generic construction of SME based on any IPE, we show that their construction of SME for small attribute space was incorrect and provide an attack that breaks the attribute hiding security, a baseline security notion for predicate encryptions that captures the privacy of the attribute associated with the ciphertext. Then, we propose a generalized construction of SME and prove that the attribute hiding security can not be achieved even in the newly defined setting. Finally, we further extend our generalized construction of SME and propose a SME that achieves the attribute hiding property even when the attribute space is small. In exchange our proposed scheme does not yield function privacy and the construction is rather inefficient. Although we did not succeed in constructing a SME both yielding function privacy and attribute hiding security, ours is the first attribute hiding SME scheme whose attribute space is polynomial in the security parameter, and we formalized a richer framework for constructing SMEs and discovered a trade-off like relationship between the two security notions.

We propose a new Bloom filter that efficiently filters out non-members. With extra bits assigned and asymmetrically distributed, the new filter reduces hash computations and memory accesses. For an error rate of 10-6, the new filter reduces cost by 31.31% with 4.33% additional space, while the standard method saves offers a 20.42% reduction.

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.

We propose a lightweight scheme for fault diagnosis in time-triggered (TT) systems. An existing scheme is preferable in its capability but incurs computation time that can be prohibitively large for some real-time systems, such as automotive control systems. Our proposed scheme, which we call voting sharing, can substantially reduce the computation time by sharing the diagnosis result obtained by each node with all nodes in the system. We clarify the properties of the voting sharing scheme with respect to fault tolerance and show some experimental results.

Recently, numerous service discovery protocols have been introduced in the open literature. Unfortunately, many of them did not consider security issues, and for those that did, many security and privacy problems still remain. One important issue is to protect the privacy of a service provider while enabling an end-user to search an alternative service using multiple keywords. To deal with this issue, the existing protocols assumed that a directory server should be trusted or owned by each service provider. However, an adversary may compromise the directory server due to its openness property. In this paper, we suggest an efficient verification of service subscribers to resolve this issue and analyze its performance and security. Using this method, we propose an efficient and secure service discovery protocol protecting the privacy of a service provider while providing multiple keywords search to an end-user. Also, we provide performance and security analysis of our protocol.

In this paper, a novel visual signal reliability (VSR) measure is proposed to consider video degradation at the signal level in audio-visual speaker identification (AVSI). The VSR estimation is formulated using a~ Gaussian fuzzy membership function (GFMF) to measure lighting variations. The variance parameters of GFMF are optimized in order to maximize the performance of the overall AVSI. The experimental results show that the proposed method outperforms the score-based reliability measuring technique.

This paper presents a method to monitor information of a large-sized multicast group that can follow the group's dynamics in real-time while avoiding feedback implosion by using probabilistic polling. In particular, this paper improves the probabilistic-polling-based approach by deriving a reference mean value as the reference control value for the number of expected feedback from the properties of a binomial estimation model. As a result, our method adaptively changes its estimation parameters depending on the feedback from receivers in order to achieve a fast estimate time with high accuracy, while preventing the possible occurrence of feedback implosion. Our experimental implementation and evaluation on PlanetLab showed that the proposed method effectively controls the number of feedback and accurately estimates the size of a dynamic multicast group.

We propose an efficient reachability estimation scheme for group membership services in mobile ad hoc networks (MANETs). The periodical message exchange-based scheme, i.e., a typical reachability estimation scheme, requires message exchanges even when the reachability status does not change. It is presumed that the reachability between nodes is maintained while the nodes move around in a limited range. The proposed scheme exploits a virtual grid for the course-grained estimation. A region in the virtual grid can be used to represent the movement range which does not change the reachability. Each node calculates how long it will stay in a region, and issues the duration information only when it gets out of the current region.

This paper presents a new Fuzzy Logic Controller (FLC) having the ability to support rational-powered membership functions. These functions are extended forms of triangular/trapezoidal membership functions, and also those functions which are generated by applying linguistic hedges. A two-input, single-output, nine-rule Takagi-Sugeno-Kang (TSK) type FLC is designed in 0.35 µm standard CMOS technology. This controller can also be used as a standard (Mamdani) type FLC having singleton output membership functions, as well as a Linguistic Hedge FLC (LHFLC). Mixed analog/digital realization of the circuit makes the design programmable and extendable, while having relatively low power consumption. Current mode realization of the circuits leads to simple and intuitive configurations. For a particular set of programming parameters, simulation results of the controller using HSPICE simulator and level 49 parameters (BSIM3v3), show an average power consumption of 5 mW, and an RMS error of 1.32% compared to ideal results obtained from MATLAB software.

Using a pair of matched square-root-raised-cosine (SRRC) filters in the transmitter and the receiver in a band-limited digital communication system can theoretically achieve zero inter-symbol interference (ISI). In reality, the ISI cannot be zero when both SRRC filters are approximately implemented because of some numerical precision problems in the design phase as well as in the implementation phase. In this paper, the author proposes an iterative method to design the coefficients of SRRC FIR filters. The required ISI of the system can be specified such that both ISI and frequency domain specifications are monitored in the design phase. Since the ISI can be specified beforehand, the tradeoff between performance and the filter length becomes possible in the proposed design algorithm.

Group signature schemes with membership revocation have been intensively researched. However, signing and/or verification of some existing schemes have computational costs of O(R), where R is the number of revoked members. Existing schemes using a dynamic accumulator or a similar technique have efficient signing and verifications with O(1) complexity. However, before signing, the signer has to modify his secret key with O(N) or O(R) complexity, where N is the group size. Therefore, for larger groups, signers suffer from enormous costs. On the other hand, an efficient scheme for middle-scale groups with about 1,000 members is previously proposed, where the signer need not modify his secret key. However this scheme also suffers from heavy signing/verification costs for larger groups with more than 10,000 members. In this paper, we adapt the middle-scale scheme to larger groups ranging from 1,000 to 1,000,000 members. At the sacrifice of the group manager's slight cost, our signing/verification is sufficiently efficient.

This paper proposes a group signature scheme with efficient membership revocation. Though group signature schemes with efficient membership revocation based on a dynamic accumulator were proposed, the previous schemes force a member to change his secret key whenever he makes a signature. Furthermore, for the modification, the member has to obtain a public membership information of O(nN) bits, where n is the length of the RSA modulus and N is the total number of joining members and removed members. In our scheme, the signer needs no modification of his secret, and the public membership information has only K bits, where K is the maximal number of members. Then, for middle-scale groups with the size that is comparable to the RSA modulus size (e.g., up to about 1000 members for 1024 bit RSA modulus), the public membership information is a single small value only, while the signing/verification also remains efficient.

The purpose of this paper is to present the novel technique to solve the recognition errors in invariant range image multi-pose face recognition. The scale, center and pose error problems were solved by using the geometric transform. Range image face data (RIFD) was obtained from a laser range finder and was used in the model to generate multi-poses. Each pose data size was reduced by linear reduction. The reduced RIFD was transformed to the gradient face model for facial feature image extraction and also for matching using the Membership Matching Score model. Using this method, the results from the experiment are acceptable although the size of gradient face image data is quite small (659 elements). Three-Layer Matching Search was the algorithm designed to reduce the access timing to the most accurate and similar pose position. The proposed algorithm was tested using facial range images from 130 people with normal facial expressions and without eyeglasses. The results achieved the mean success rate of 95.67 percent of 12 degrees up/down and left/right (UDLR) and 88.35 percent of 24 degrees UDLR.

Many fuzzy traffic controllers adjust the extension time of the green phase with the fuzzy input variables, arrival and queue. However, in our experiments, we found that the two input variables are not sufficient for an intersection where traffic flow rates change and thus, in this paper, traffic volume is used as an additional variable. Traffic volume is defined as the number of vehicles entering an intersection every second. In designing a fuzzy traffic controller, an ad-hoc approach is usually used to find membership functions and fuzzy control rules showing good performance. That is, initial ones are generated by human operators and modified many times based on the results of simulation. To partially overcome the limitations of the ad-hoc approach, we use genetic algorithms to automatically determine the membership functions for terms of each fuzzy variable when fuzzy control rules are given by hand. The experimental results indicate that a fuzzy logic controller with volume variable outperforms conventional ones with no volume variable in terms of the average delay and the average velocity. Also, the controller shows better performance when membership functions generated by a genetic algorithms instead of ones generated by hand are used.

Correct and quick generation of a membership function is the key point when we implement a real-time fuzzy logic controller. In this Letter, we presented two efficient VLSI architectures, one to generate triangle-shaped and the other to generate trapezoid-shaped membership functions. Simulation results show that our designs require lower hardware cost but achieve faster working rate.