In the health IoT (Internet of Things), the specialized sensor devices can be used to monitor remote health and notify the emergency information, e.g., blood pressure, heart rate, etc. These data can help the doctors to rescue the patients. In cloud-based health IoT, patients' medical/health data is managed by the cloud service providers. Secure storage and privacy preservation are indispensable for the outsourced medical/health data in cloud computing. In this paper, we study the integrity checking and sharing of outsourced private medical/health records for critical patients in public clouds (ICS). The patient can check his own medical/health data integrity and retrieve them. When a patient is in coma, some authorized entities and hospital can cooperate to share the patient's necessary medical/health data in order to rescue the patient. The paper studies the system model, security model and concrete scheme for ICS in public clouds. Based on the bilinear pairing technique, we design an efficient ICS protocol. Through security analysis and performance analysis, the proposed protocol is provably secure and efficient.

In the current IoT (Internet of Things) environment, more and more Things: devices, objects, sensors, and everyday items not usually considered computers, are connected to the Internet, and these Things affect and change our social life and economic activities. By using IoTs, service providers can collect and store personal information in the real world, and such providers can gain access to detailed behaviors of the user. Although service providers offer users new services and numerous benefits using their detailed information, most users have concerns about the privacy and security of their personal data. Thus, service providers need to take countermeasures to eliminate those concerns. To help eliminate those concerns, first we conduct a survey regarding users' privacy and security concerns about IoT services, and then we analyze data collected from the survey using structural equation modeling (SEM). Analysis of the results provide answers to issues of privacy and security concerns to service providers and their users. And we also analyze the effectiveness and effects of personal information management and protection functions in IoT services.

With the flourish of applications based on the Internet of Things (IoT), privacy issues have been attracting a lot of attentions. Although the concept of privacy homomorphism was proposed along with the birth of the well-known RSA cryptosystems, cryptographers over the world have spent about three decades for finding the first implementation of the so-called fully homomorphic encryption (FHE). Despite of, currently known FHE schemes, including the original Gentry's scheme and many subsequent improvements as well as the other alternatives, are not appropriate for IoT-oriented applications because most of them suffer from the problems of inefficient key size and noisy restraining. In addition, for providing fully support to IoT-oriented applications, symmetric fully homomorphic encryptions are also highly desirable. This survey presents an analysis on the challenges of designing secure and practical FHE for IoT, from the perspectives of lightweight requirements as well as the security requirements. In particular, some issues about designing noise-free FHE schemes would be addressed.

Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.

Buffer overflow is one of the main approaches to get control of vulnerable programs. This paper presents a protection technique called BFWindow for performance and resource sensitive embedded systems. By coloring data structure in memory with single associate property bit to each byte and extending the target memory block to a BFWindow(2), it validates each memory write by speculatively checking consistency of data properties within the extended buffer window. Property bits are generated by compiler statically and checked by hardware at runtime. They are transparent to users. Experimental results show that the proposed mechanism is effective to prevent sequential memory writes from crossing buffer boundaries which is the common scenario of buffer overflow exploitations. The performance overhead for practical protection mode across embedded system benchmarks is under 1%.

In this paper, a multi-data and multi-ACK verified selective forwarding attacks (SFAs) detection scheme is proposed for containing SFAs. In our scheme, each node (in addition to the nodes in the hotspots area) generates multiple acknowledgement (ACK) message for each received packet to confirm the normal packet transmission. In multiple ACK message, one ACK is returned along the data forwarding path, other ACKs are returned along different routing paths, and thus malicious nodes can be located accurately. At the same time, source node send multiple data routing, one is primary data routing, the others are backup data routing. Primary data is routed to sink directly, but backup data is routed to nodes far from sink, and then waits for the returned ACK of sink when primary data is routed to sink. If a node doesn't receive the ACK, the backup data is routed to sink, thus the success rate of data transmission and lifetime can be improved. For this case, the MDMA scheme has better potential to detect abnormal packet loss and identify suspect nodes as well as resilience against attack. Theoretical analysis and experiments show that MDMA scheme has better ability for ensuring success rate of data transmission, detecting SFA and identifying malicious nodes.

With the rapid development of Internet of things (IoT), Radio Frequency Identification (RFID) has become one of the most significant information technologies in the 21st century. However, more and more privacy threats and security flaws have been emerging in various vital RFID systems. Traditional RFID systems only focus attention on foundational implementation, which lacks privacy protection and effective identity authentication. To solve the privacy protection problem this paper proposes a privacy protection method with a Privacy Enhancement Model for RFID (PEM4RFID). PEM4RFID utilizes a “2+2” identity authentication mechanism, which includes a Two-Factor Authentication Protocol (TFAP) based on “two-way authentication”. Our TFAP employs “hardware information + AES-ECC encryption”, while the ”“two-way authentication” is based on improved Combined Public Key (CPK). Case study shows that our proposed PEM4RFID has characteristics of untraceability and nonrepeatability of instructions, which realizes a good trade-off between privacy and security in RFID systems.

Radio Frequency Identification (RFID) plays a crucial role in IoT development. With the extensive use of RFID, the fact that a single RFID tag integrates multiple applications has become a mainstream. To facilitate users to use the multi-application RFID tag and revoke some applications in the tag securely and efficiently, a secure RFID application revocation scheme is proposed in this paper. In the scheme, each response for the challenge between tag and reader is different, and a group has the feature of many tags. Even if the group index number and corresponding group are revealed, a specific tag does not be precisely found and tracked. Users are anonymous completely. The scheme also allows users to set the validity period for an application or some applications. If the application contains the validity period and expires, the server will remove the validity period and revoke the application automatically in the tag when the RFID tag accesses server again. The proposed scheme cannot only be used in multi-application RFID tag but also be used in one-application RFID tag. Furthermore, compared with other existing schemes, the scheme provides a higher level of security and has an advantage of performance. Our scheme has the ability of mutual authentication and Anti-replay by adding a random number r₂, and it is easy to against synchronization attack. Security proof is given in our paper and performance advantage are mainly reflected in the following points such as forward security, synchronization, storage complexity, computational complexity, etc. Finally, the proposed scheme can be used in multi-application RFID tag to promote the development of the IoT.

With the increase of data quantity, people have begun to attach importance to cloud storage. However, numerous security accidents occurred to cloud servers recently, thus triggering thought about the security of traditional single cloud. In other words, traditional single cloud can't ensure the privacy of users' data to a certain extent. To solve those security issues, multi-cloud systems which spread data over multiple cloud storage servers emerged. They employ a series of erasure codes and other keyless dispersal algorithms to achieve high-level security. But non-systematic codes like RS require relatively complex arithmetic, and systematic codes have relatively weaker security. In terms of keyless dispersal algorithms, they avoid key management issues but not suit to complete parallel optimization or deduplication which is important to limited cloud storage resources. So in this paper, we design a new kind of XOR-based non-systematic erasure codes - Privacy Protecting Codes (PPC) and a SIMD encoding algorithm for better performance. To achieve higher-level security, we put forward a novel deduplication-friendly dispersal algorithm called Hash Cyclic Encryption-PPC (HCE-PPC) which can achieve complete parallelization. With these new technologies, we present a multi-cloud storage system called CloudS. For better user experience and the tradeoffs between security and performance, CloudS provides multiple levels of security by various combinations of compression, encryption and coding schemes. We implement CloudS as a web application which doesn't require users to perform complicated operations on local.

Oblivious RAM is a technique for hiding the access patterns between a client and an untrusted server. However, current ORAM algorithms incur large communication or storage overhead. We propose a novel ORAM construction using a matrix logical structure for server storage where a client downloads blocks from each row, choosing the column randomly to hide the access pattern. Both a normal construction and recursive construction, where a position map normally stored on the client is also stored on the server, are presented. We show our matrix ORAM achieves constant bandwidth cost for the normal construction, uses similar storage to the existing Path ORAM, and improves open the bandwidth cost compared to Path ORAM under certain conditions in the recursive construction.

By the deployment of Internet of Things, embedded systems using microcontroller are nowadays under threats through the network and incorporating security measure to the systems is highly required. Unfortunately, microcontrollers are not so powerful enough to execute standard security programs and need light-weight, high-speed and secure cryptographic libraries. In this paper, we port NaCl cryptographic library to ARM Cortex-M0(M0+) Microcontroller, where we put much effort in fast and secure implementation. Through the evaluation we show that the implementation achieves about 3 times faster than AVR NaCl result and reduce half of the code size.

In the classical computation theory, the language of a system features the computational behavior of the system but it does not distinguish the determinism and nondeterminism of actions. However, Milner found that the determinism and nondeterminism affect the interactional behavior of interactive systems and thus the notion of language does not features the interactional behavior. Therefore, Milner proposed the notion of (weak) bisimulation to solve this problem. With the development of internet, more and more interactive systems occur in the world, such as electronic trading system. Security is one of the most important topics for these systems. We find that different security policies can also affect the interactional behavior of a system, which exactly is the reason why a good policy can strengthen the security. In other words, two interactive systems with different security policies are not of an equivalent behavior although their functions (or business processes) are identical. However, the classic (weak) bisimulation theory draws an opposite conclusion that their behaviors are equivalent. The notion of (weak) bisimulation is not suitable for these security-oriented interactive systems since it does not consider a security policy. This paper proposes the concept of secure bisimulation in order to solve the above problem.

In data sharing privacy has become one of the main concerns particularly when sharing datasets involving individuals contain private sensitive information. A model that is widely used to protect the privacy of individuals in publishing micro-data is k-anonymity. It reduces the linking confidence between private sensitive information and specific individual by generalizing the identifier attributes of each individual into at least k-1 others in dataset. K-anonymity can also be defined as clustering with constrain of minimum k tuples in each group. However, the accuracy of the data in k-anonymous dataset decreases due to huge information loss through generalization and suppression. Also most of the current approaches are designed for numerical continuous attributes and for categorical attributes they do not perform efficiently and depend on attributes hierarchical taxonomies, which often do not exist. In this paper we propose a new model for k-anonymization, which is called Similarity-Based Clustering (SBC). It is based on clustering and it measures similarity and calculates distances between tuples containing numerical and categorical attributes without hierarchical taxonomies. Based on this model a bottom up greedy algorithm is proposed. Our extensive study on two real datasets shows that the proposed algorithm in comparison with existing well-known algorithms offers much higher data utility and reduces the information loss significantly. Data utility is maintained above 80% in a wide range of k values.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.

Network Intrusion Detection Systems (NIDS) are deployed to protect computer networks from malicious attacks. Proper evaluation of NIDS requires more scrutiny than the evaluation for general network appliances. This evaluation is commonly performed by sending pre-generated traffic through the NIDS. Unfortunately, this technique is often limited in diversity resulting in evaluations incapable of examining the complex data structures employed by NIDS. More sophisticated methods that generate workload directly from NIDS rules consume excessive resources and are incapable of running in real-time. This work proposes a novel approach to real-time workload generation for NIDS evaluation to improve evaluation diversity while maintaining much higher throughput. This work proposes a generative grammar which represents an optimized version of a context-free grammar derived from the set of strings matching to the given NIDS rule database. The grammar is memory-efficient and computationally light when generating workload. Experiments demonstrate that grammar-generated workloads exert an order of magnitude more effort on the target NIDS. Even better, this improved diversity comes at much smaller cost in memory and speeds four times faster than current approaches.

A network-based remote host clock skew measurement involves collecting the offsets, the differences between sending and receiving times, of packets from the host within a period of time. Although the variant and immeasurable delay in each packet prevents the measurer from getting the real clock offset, the local minimum delays and the majority of delays delineate the clock offset shifts, and are used by existing approaches to estimate the skew. However, events during skew measurement like time synchronization and rerouting caused by switching network interface or base transceiver station may break the trend into multi-segment patterns. Although the skew in each segment is theoretically of the same value, the skew derived from the whole offset-set usually differs with an error of unpredictable scale. In this work, a method called dynamic region of offset majority locating (DROML) is developed to detect multi-segment cases, and to precisely estimate the skew. DROML is designed to work in real-time, and it uses a modified version of the HT-based method [8] both to measure the skew of one segment and to detect the break between adjacent segments. In the evaluation section, the modified HT-based method is compared with the original method and with a linear programming algorithm (LPA) on accumulated-time and short-term measurement. The fluctuation of the modified method in the short-term experiment is 0.6 ppm (parts per million), which is obviously less than the 1.23 ppm and 1.45 ppm from the other two methods. DROML, when estimating a four-segment case, is able to output a skew of only 0.22 ppm error, compared with the result of the normal case.

This paper proposes a blind, inaudible, robust digital-audio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.

Since many cyber-physical systems (CPSs) manipulate security-sensitive data, enhancing the quality of security in a CPS is a critical and challenging issue in CPS design. Although there has been a large body of research on securing general purpose PCs, directly applying such techniques to a CPS can compromise the real-time property of CPSs since the timely execution of tasks in a CPS typically relies on real-time scheduling. Recognizing this property, previous works have proposed approaches to add a security constraint to the real-time properties to cope with the information leakage problem that can arise between real-time tasks with different security levels. However, conventional works have mainly focused on non-preemptive scheduling and have suggested a very naive approach for preemptive scheduling, which shows limited analytical capability. In this paper, we present a new preemptive fixed-priority scheduling algorithm incorporating a security constraint, called lowest security-level first (LSF) and its strong schedulability analysis to reduce the potential of information leakage. Our simulation results show that LSF schedulability analysis outperforms state-of-the-art FP analysis when the security constraint has reasonable timing penalties.

In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.

Massive Open Online Courses (MOOC) have been invented to support Virtual Learning Environment (VLE) for higher education. While numerous learning courses and contents were authored, most of the existing resources are now hard to reuse/redistribute among instructors due to the privacy of the contents. Therefore, Open Educational Resources (OER) and the Creative Commons license (CC) are interesting solutions available to alleviate such problems of MOOC. This research presents a new framework that effectively connects OER and MOOC for a life-long e-Learning platform for Thai people. We utilize the Fedora Commons repository for an OER back-end, and develop a new front-end to manage OER resources. In addition, we introduce a “FedX API” - including a packet encapsulation and a data transmission module - that organizes educational resources between both systems. We also proposed the CC declaring function to help participants on-the-fly declare their content license; therefore, any resources must be granted as an open licensing. Another important function is a Central Authorized System (CAS) which is applied to develop single signing-on to facilitate the OER-MOOC connection. Since the framework is designed to support the massive demand, the concurrent access capability is also evaluated to measure the performance of the proposed framework. The results show that the proposed framework can provide up to 750 concurrencies without any defects. The FedX API does not produce bottleneck trouble on the interoperability framework in any cases. In addition, resources can be exchanged among the third-party OER repositories by an OAI-PMH harvesting tool.

We have developed software that uses the R statistics software environment to automatically generate tactile graphs — i.e. graphs that can be read by blind people using their sense of touch. We released this software as a Web application to make it available to anyone, from anywhere. This Web application can automatically generate images for tactile graphs from numerical data in a CSV file. It is currently able to generate four types of graph — scatter plots, line graphs, bar charts and pie charts. This paper describes the Web application's functions, operating procedures and the results of evaluation experiments.

Depth-based action recognition has been attracting the attention of researchers because of the advantages of depth cameras over standard RGB cameras. One of these advantages is that depth data can provide richer information from multiple projections. In particular, multiple projections can be used to extract discriminative motion patterns that would not be discernible from one fixed projection. However, high computational costs have meant that recent studies have exploited only a small number of projections, such as front, side, and top. Thus, a large number of projections, which may be useful for discriminating actions, are discarded. In this paper, we propose an efficient method to exploit pools of multiple projections for recognizing actions in depth videos. First, we project 3D data onto multiple 2D-planes from different viewpoints sampled on a geodesic dome to obtain a large number of projections. Then, we train and test action classifiers independently for each projection. To reduce the computational cost, we propose a greedy method to select a small yet robust combination of projections. The idea is that best complementary projections will be considered first when searching for optimal combination. We conducted extensive experiments to verify the effectiveness of our method on three challenging benchmarks: MSR Action 3D, MSR Gesture 3D, and 3D Action Pairs. The experimental results show that our method outperforms other state-of-the-art methods while using a small number of projections.

Analyzing the schedulability of hierarchical real-time systems is difficult because of the systems' complex behavior. It gets more complicated when shared resources or dependencies among tasks are included. This paper introduces a framework based on UPPAAL that can analyze the schedulability of hierarchical real-time systems.

Recently in an SDN/NFV-enabled network, a consolidated middlebox is proposed in which middlebox functions required by a network flow are provided at a single machine in a virtualized manner. With the promising advantages such as simplifying network traffic routing and saving resources of switches and machines, consolidated middleboxes are going to replace traditional middleboxes in the near future. However, the location of consolidated middleboxes may affect the performance of an SDN/NFV network significantly. Accordingly, the consolidated middlebox positioning problem in an SDN/NFV-enabled network must be addressed adequately with service chain constraints (a flow must visit a specific type of consolidated middlebox), resource constraints (switch memory and processing power of the machine), and performance requirements (end-to-end delay and bandwidth consumption). In this paper, we propose a novel solution of the consolidated middlebox positioning problem in an SDN/NFV-enabled network based on flow clustering to improve the performance of service chain flows and utilization of a consolidated middlebox. Via extensive simulations, we show that our solution significantly reduces the number of routing rules per switch, the end-to-end delay and bandwidth consumption of service flows while meeting service chain and resource constraints.

We reported a secure scan design approach using shift register equivalents (SR-equivalents, for short) that are functionally equivalent but not structurally equivalent to shift registers [10 and also introduced generalized shift registers (GSRs, for short) to apply them to secure scan design [11]-[13]. In this paper, we combine both concepts of SR-equivalents and GSRs and consider the synthesis problem of SR-equivalent GSRs, i.e., how to modify a given GSR to an SR-equivalent GSR. We also consider the enumeration problem of SR-equivalent GFSRs, i.e., the cardinality of the class of SR-equivalent GSRs to clarify the security level of the secure scan architecture.

To improve the recognition rate of the speech emotion, new spectral features based on local Hu moments of Gabor spectrograms are proposed, denoted by GSLHu-PCA. Firstly, the logarithmic energy spectrum of the emotional speech is computed. Secondly, the Gabor spectrograms are obtained by convoluting logarithmic energy spectrum with Gabor wavelet. Thirdly, Gabor local Hu moments(GLHu) spectrograms are obtained through block Hu strategy, then discrete cosine transform (DCT) is used to eliminate correlation among components of GLHu spectrograms. Fourthly, statistical features are extracted from cepstral coefficients of GLHu spectrograms, then all the statistical features form a feature vector. Finally, principal component analysis (PCA) is used to reduce redundancy of features. The experimental results on EmoDB and ABC databases validate the effectiveness of GSLHu-PCA.

In this letter, a local characteristic image restoration based on convolutional neural network is proposed. In this method, image restoration is considered as a classification problem and images are divided into several sub-blocks. The convolutional neural network is used to extract and classify the local characteristics of image sub-blocks, and the different forms of the regularization constraints are adopted for the different local characteristics. Experiments show that the image restoration results by the regularization method based on local characteristics are superior to those by the traditional regularization methods and this method also has lower computing cost.

In the self-similarity super resolution (SR) approach, similar examples are searched across down-scales in the image pyramid, and the computations of searching similar examples are very heavy. This makes it difficult to work in a real-time way under common software implementation. Therefore, the search process should be further accelerated at an algorithm level. Cauchy-Schwarz inequality has been used previously for fast vector quantization (VQ) encoding. The candidate patches in the search region of SR are analogous to the code-words in the VQ, and Cauchy-Schwarz inequality is exploited to exclude implausible candidate patches early. Consequently, significant acceleration of the similar patch search process is achieved. The proposed method can easily make an optimal trade-off between running speed and visual quality by appropriately configuring the bypass-threshold.