Recent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Publishing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as “How many people are in Kyoto Station now?” However, analyzing these raw data will entail risks of compromising individual privacy. ε-Differential Privacy has emerged as a well-known standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. However, since user trajectories will be generated infinitely, it is difficult to protect every trajectory under ε-differential privacy. On the other hand, in real life, not all users require the same level of privacy. To this end, we propose a flexible privacy model of l-trajectory privacy to ensure every desired length of trajectory under protection of ε-differential privacy. We also design an algorithmic framework to publish l-trajectory private data in real time. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.

Various incidents expose the vulnerability and fragility of the Internet inter-domain routing, and highlight the need for further efforts in developing new approaches to evaluating the trustworthiness of routing information. Based on collections of BGP routing information, we disclose a variety of anomalies and malicious attacks and demonstrate their potential impacts on the Internet security. This paper proposes a systematic approach to detecting the anomalies in inter-domain routing, combining effectively spatial-temporal multiple-view method, knowledge-based method, and cooperative verification method, and illustrates how it helps in alleviating the routing threats by taking advantage of various measures. The main contribution of our approach lies on critical techniques including the construction of routing information sets, the design of detection engines, the anomaly verification and the encouragement mechanism for collaboration among ASs. Our approach has been well verified by our Internet Service Provider (ISP) partners and has been shown to be effective in detecting anomalies and attacks in inter-domain routing.

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.

The significance of individuals' location information has been increasing recently, and the utilization of such data has become indispensable for businesses and society. The possible uses of location information include personalized services (maps, restaurant searches and weather forecast services) and business decisions (deciding where to open a store). However, considering that the data could be exploited, users should add random noise using their terminals before providing location data to collectors. In numerous instances, the level of privacy protection a user requires depends on their location. Therefore, in our framework, we assume that users can specify different privacy protection requirements for each location utilizing the adversarial error (AE), and the system computes a mechanism to satisfy these requirements. To guarantee some utility for data analysis, the maximum error in outputting the location should also be output. In most privacy frameworks, the mechanism for adding random noise is public; however, in this problem setting, the privacy protection requirements and the mechanism must be confidential because this information includes sensitive information. We propose two mechanisms to address privacy personalization. The first mechanism is the individual exponential mechanism, which uses the exponential mechanism in the differential privacy framework. However, in the individual exponential mechanism, the maximum error for each output can be used to narrow down candidates of the actual location by observing outputs from the same location multiple times. The second mechanism improves on this deficiency and is called the donut mechanism, which uniformly outputs a random location near the location where the distance from the user's actual location is at the user-specified AE distance. Considering the potential attacks against the idea of donut mechanism that utilize the maximum error, we extended the mechanism to counter these attacks. We compare these two mechanisms by experiments using maps constructed from artificial and real world data.

We present a simple decoding algorithm that modifies soft bit-flipping algorithm for decoding LDPC codes. In our method, a new parameter is explored to distinguish the variables (symbols) belonging to the same number of unsatisfied constraints. A token is also assigned in the method to avoid repeated flipping of the same variable, rather than using a constant taboo length. Our scheme shows a similar computational load as the taboo-based algorithm, while having a similar decoding performance as the belief propagation algorithm.

Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.

Discrete Wavelet Multi-carrier Transceiver (DWMT) system, which can be viewed as a kind of OFDM, has many advantages because it uses wavelets as its base functions. In this paper we present a new sub-carrier frequency offset correction method for DWMT systems with little assistant information. The essential ideal of this algorithm is: when an orthogonal multi-carrier system is of perfect frequency synchronize, the demodulated signals of different sub-carriers are independent of each other. Whereas when frequency offset exists, intercarrier interference will distort the demodulated signal, i.e. every demodulated signal is the sum of several modulated signals' projects on the demodulating frequency. So the adjacent demodulated signals consist of the element of the same modulated signal, and these demodulated signals are correlated with each other. The degree that they correlated with each other depends on sub-carrier relative frequency offset. Since that little assistant information is used in this algorithm the spectrum efficiency can be largely increased. Simulation results shown that if the number of the sub-carrier of the DWMT system is bigger than 1000, the relative frequency offset can be limited in 2%.