In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leakage of the workers. Local differential privacy (LDP) is currently the latest technique to protect data privacy. However, most of LDP-based schemes have limitations in providing good utility due to extensive noise in perturbing trajectories. In this work, to balance the privacy and utility, we propose a novel pattern-aware privacy protection method called trajectory-aware privacy-preserving with local differential privacy (TALDP). The key idea is that, rather than applying the same degree of perturbation to all location points, we employ adaptive privacy budget allocation, assigning varied privacy budgets to individual location points, thereby mitigating the perturbation’s impact and enhancing overall utility. Meanwhile, to ensure the privacy, we give the different perturbing points to different privacy budgets according to their important degree for the patterns of the trajectories. In particular, we use Karman filter method to select the important location points and decide their privacy budgets. We conduct extensive experiments on three real datasets. The results show that our approach improves the utility over many other current methods while still provide good the privacy protection.

Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks’ location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies$\epsilon$-differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.

Summary The fast‐growing Android application market has attracted more and more application developers. However, many plagiarists use decompiled tools to modify original applications to get clones, which has become a serious threat. For detecting cloned applications, most of the existing schemes do not consider the detected accuracy and time consumption at the same time. In this article, we propose a two‐phase detection scheme to achieve fast and accurate clone detection in large‐scale applications. In the rapid screening phase, a fix‐length minhash summary is constructed for each application and the locality‐sensitive hashing (LSH) algorithm is used to obtain suspicious cloned applications quickly. In the accurate detection phase, by merging and pruning the layout and interaction information of all user interfaces (UIs) at the application runtime, we obtain the birthmark named merged layout tree (MLT), which can resist nested obfuscation and repacking attack. Finally, cloned apps are detected by calculating the similarity between MLTs from suspicious cloned apps. We evaluate our detection scheme in two app datasets (nearly 170,000 Android applications) and compare it with the state‐of‐the‐art clone detection methods. Extensive experiments show that our method has high accuracy and efficiency for clone detection in large‐scale apps.

Trending topic recommendation (TTR) has become a popular social service for social users to obtain interesting topics based on public social content. Due to sensitive privacy, the traditional differential privacy (DP) methods are used to protect social contents. However, these DP methods rely on a fully trusted third party (TTP) without considering protecting the correlations of social keywords, and do not support the privacy-preserving online social content publication. In this article, we propose a novel local DP-based TTR (LDPTR) scheme to perform high-quality online TTR services while achieving local privacy-preserving social contents. Specifically, LDPTR first clusters the social keywords with high correlations into noisy graph classes based on a graph-based LDP (GLDP) algorithm for ensuring the keyword correlation privacy. Second, LDPTR adopts a novel mechanism called ∈2-compressive sensing indistinguishability (CSI) to generate noisy social topics, thereby preventing the user-linkage attack and breaking the curse of high-dimensional local differential privacy (LDP). Then, a dynamic graph-based CSI (DGCSI) algorithm is proposed to protect the online social content privacy while ensuring data usability. Furthermore, LDPTR calculates the trending topics of interest with high burstiness by using a topic distribution similarity model-based topic burstiness (TMTB) algorithm, which achieves effective TTR services. Our LDPTR scheme satisfies ∈-LDP by detailed security analysis. Extensive experiments over two real-world data sets show that the proposed LDPTR gets high data utility while ensuring high-level privacy.

The e-healthcare cloud system has shown its potential to improve the quality of healthcare and individuals' quality of life. Unfortunately, security and privacy impede its widespread deployment and application. There are several research works focusing on preserving the privacy of the electronic healthcare record (EHR) data. However, these works have two main limitations. First, they only support the `black or white' access control policy. Second, they suffer from the inference attack. In this paper, for the first time, we design an inference attack-resistant e-healthcare cloud system with fine-grained access control. We first propose a two-layer encryption scheme. To ensure an efficient and fine-grained access control over the EHR data, we design the first-layer encryption, where we devise a specialized access policy for each data attribute in the EHR, and encrypt them individually with high efficiency. To preserve the privacy of role attributes and access policies used in the first-layer encryption, we systematically construct the second-layer encryption. To take full advantage of the cloud server, we propose to let the cloud execute computationally intensive works on behalf of the data user without knowing any sensitive information. To preserve the access pattern of data attributes in the EHR, we further construct a blind data retrieving protocol. We also demonstrate that our scheme can be easily extended to support search functionality. Finally, we conduct extensive security analyses and performance evaluations, which confirm the efficacy and efficiency of our schemes.

In this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features. Compared with the existing dynamic texture recognition approaches using carefully designed features for high performance, our method use only a few 3D random filters to extract spatio-temporal features from local dynamic texture blocks, which are further encoded into a low-dimensional feature vector. To explore the representative power of the 3D random features, we use two different encoding schemes, the learning-based Fisher vector encoding and the learning-free binary encoding. The proposed method is tested on the UCLA and DynTex databases with various evaluation protocols. Experimental results demonstrate the high performance of our method for dynamic texture recognition.

Since the threat of malicious software (malware) has become increasingly serious, automatic malware detection techniques have received increasing attention, where machine learning (ML)-based visualization detection methods become more and more popular. In this paper, we demonstrate that the state-of-the-art ML-based visualization detection methods are vulnerable to Adversarial Example (AE) attacks. We develop a novel Adversarial Texture Malware Perturbation Attack (ATMPA) method based on the gradient descent and L-norm optimization method, where attackers can introduce some tiny perturbations on the transformed dataset such that ML-based malware detection methods will completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the accuracy rate down to 0% for several ML-based detection methods, and the rate of transferability is 74.1% on average.

Local binary descriptors, such as local binary pattern (LBP) and its various variants, have been studied extensively in texture and dynamic texture analysis due to their outstanding characteristics, such as grayscale invariance, low computational complexity and good discriminability. Most existing local binary feature extraction methods extract spatio-temporal features from three orthogonal planes of a spatio-temporal volume by viewing a dynamic texture in 3D space. For a given pixel in a video, only a proportion of its surrounding pixels is incorporated in the local binary feature extraction process. We argue that the ignored pixels contain discriminative information that should be explored. To fully utilize the information conveyed by all the pixels in a local neighborhood, we propose extracting local binary features from the spatio-temporal domain with 3D filters that are learned in an unsupervised manner so that the discriminative features along both the spatial and temporal dimensions are captured simultaneously. The proposed approach consists of three components: 1) 3D filtering; 2) binary hashing; and 3) joint histogramming. Densely sampled 3D blocks of a dynamic texture are first normalized to have zero mean and are then filtered by 3D filters that are learned in advance. To preserve more of the structure information, the filter response vectors are decomposed into two complementary components, namely, the signs and the magnitudes, which are further encoded separately into binary codes. The local mean pixels of the 3D blocks are also converted into binary codes. Finally, three types of binary codes are combined via joint or hybrid histograms for the final feature representation. Extensive experiments are conducted on three commonly used dynamic texture databases: 1) UCLA; 2) DynTex; and 3) YUVL. The proposed method provides comparable results to, and even outperforms, many state-of-the-art methods.

Mobile payment apps are seeing explosive usage worldwide. This paper focuses on Venmo, a very popular mobile person-to-person payment service owned by Paypal. Venmo allows money transfers between users with a mandatory transaction note. More than half of transaction records in Venmo are public information. In this paper, we propose a multilayer location inference (MLLI) technique to infer user locations from public transaction records in Venmo. MLLI explores two observations. First, many Venmo transaction notes contain implicit location cues. Second, the types and temporal patterns of user transactions have strong ties to their location closeness. With a large dataset of 2.12M users and 20.23M Venmo transaction records, we show that MLLI can identify the top-1, top-3, and top-5 possible locations for a Venmo user with accuracy up to 50%, 80%, and 90%, respectively. Our results highlight the danger of sharing transaction notes on Venmo or similar mobile payment apps.

With the advent of cloud computing, more and more people tend to outsource their data to the cloud. As a fundamental data utilization, secure keyword search over encrypted cloud data has attracted the interest of many researchers recently. However, most of existing researches are based on an ideal assumption that the cloud server is “curious but honest”, where the search results are not verified. In this paper, we consider a more challenging model, where the cloud server would probably behave dishonestly. Based on this model, we explore the problem of result verification for the secure ranked keyword search. Different from previous data verification schemes, we propose a novel deterrent-based scheme. With our carefully devised verification data, the cloud server cannot know which data owners, or how many data owners exchange anchor data which will be used for verifying the cloud server's misbehavior. With our systematically designed verification construction, the cloud server cannot know which data owners' data are embedded in the verification data buffer, or how many data owners' verification data are actually used for verification. All the cloud server knows is that, once he behaves dishonestly, he would be discovered with a high probability, and punished seriously once discovered. Furthermore, we propose to optimize the value of parameters used in the construction of the secret verification data buffer. Finally, with thorough analysis and extensive experiments, we confirm the efficacy and efficiency of our proposed schemes.

In this paper, a local spatiotemporal descriptor, namely, the volume local binary count (VLBC), is proposed for the representation and recognition of dynamic texture. This descriptor, which is similar in spirit to the volume local binary pattern (VLBP), extracts histograms of thresholded local spatiotemporal volumes using both appearance and motion features to describe dynamic texture. Unlike VLBP using binary encoding, VLBC does not exploit the local structure information and only counts the number of 1s in the thresholded codes. Thus, VLBC can include more neighboring pixels without exponentially increasing the feature dimension as VLBP does. Furthermore, a completed version of VLBC (CVLBC) is also proposed to enhance the performance of dynamic texture recognition with additional information about local contrast and central pixel intensities. The proposed method is not only efficient to compute but also effective for dynamic texture representation. In experiments with three dynamic texture databases, namely, UCLA, DynTex, and DynTex++, the proposed method produces classification rates that are comparable to those produced by the state-of-the-art approaches. In addition to dynamic texture recognition, we propose utilizing CVLBC for 2-D face spoofing detection. As an effective spatiotemporal descriptor, CVLBC can well describe the differences between facial videos of valid users and impostors, thus achieving good performance for face spoofing detection. For comparison with other methods, the proposed method is evaluated on three face antispoofing databases: Print-Attack, Replay-Attack, and CAS Face Antispoofing. The experimental results demonstrate the effectiveness of CVLBC for 2-D face spoofing detection.

In this paper, we propose a novel method for dynamic texture recognition using multiscale PCA-learned filters. PCA is utilized to learn multiscale filters from image sequences on three orthogonal planes (XY, XT and YT). Filter responses that contain both spatial and temporal information at multiple scales are then encoded into a descriptor named MPCAF-TOP. The proposed method is simple to derive and implement, and also very effective for dynamic texture recognition. The proposed method is evaluated on two benchmark databases, namely UCLA and DynTex++. Experimental results show that the proposed approach is comparable to state-of-the-art methods.

Social data outsourcing is an emerging paradigm for effective and efficient access to the social data. In such a system, a third-party Social Data Provider (SDP) purchases complete social datasets from Online Social Network (OSN) operators and then resells them to data consumers who can be any individuals or entities desiring the complete social data satisfying some criteria. The SDP cannot be fully trusted and may return wrong query results to data consumers by adding fake data and deleting/modifying true data in favor of the businesses willing to pay. In this paper, we initiate the study on verifiable social data outsourcing whereby a data consumer can verify the trustworthiness of the social data returned by the SDP. We propose three schemes for verifiable queries over outsourced social data. The three schemes all require the OSN provider to generate some cryptographic auxiliary information, based on which the SDP can construct a verification object for the data consumer to verify the query-result trustworthiness. They differ in how the auxiliary information is generated and how the verification object is constructed and verified. Extensive experiments based on a real Twitter dataset confirm the high efficacy and efficiency of our schemes.

In this paper, we introduce the notion of consistent F-augmented contexts by adding a special family of finite subsets into the structure of a formal context, which essentially establishes the basis of the representation of general algebraic domains. In particular, we investigate the association rule systems which are derived from the consistent F-augmented contexts and propose the notion of formal association rule systems. By the notion of antecedent connections, we obtain the equivalence between the category of formal association rule systems and that of algebraic domains, which demonstrates that the proposed notion of formal association rule systems provides a concrete approach to representing algebraic domains.

Data deduplication is commonly adopted in cloud storage services to improve storage utilization and reduce transmission bandwidth. It, however, conflicts with the requirement for data confidentiality offered by data encryption. Hierarchical authorized deduplication alleviates the tension between data deduplication and confidentiality and allows a cloud user to perform privilege-based duplicate checks before uploading the data. Existing hierarchical authorized deduplication systems permit the cloud server to profile cloud users according to their privileges. In this paper, we propose a secure hierarchical deduplication system to support privilege-based duplicate checks and also prevent privilege-based user profiling by the cloud server. Our system also supports dynamic privilege changes. Detailed theoretical analysis and experimental studies confirm the security and high efficiency of our system.

With the advent of cloud computing, it has become increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve this data. For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. In this paper, we propose schemes to deal with privacy preserving ranked multi-keyword search in a multi-owner model (PRMSM). To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel additive order and privacy preserving function family. To prevent the attackers from eavesdropping secret keys and pretending to be legal data users submitting searches, we propose a novel dynamic secret key generation protocol and a new data user authentication protocol. Furthermore, PRMSM supports efficient data user revocation. Extensive experiments on real-world datasets confirm the efficacy and efficiency of PRMSM.

Personal Health Record (PHR) systems have been widely used to manage individuals' medical history. Meanwhile, with a rapid growth of the volume of PHRs, individuals outsource PHR systems to the cloud to facilitate management. In this paper, we consider a multi-source cloud-based PHR environment, where hospitals as the data providers are authorized to upload an individual's medical data to the cloud. In this environment, a data provider builds an index as an Multi-Dimensional B-tree from an individual's medical data for fast lookup, and encrypts both the index and data before uploading, to preserve data privacy. To achieve efficient and privacy-preserving query on the encrypted medical data in cloud computing, we propose a Multi-source Encrypted Indexes Merging (MEIM) mechanism, where the indexes encrypted with a novel Multi-source Order-Preserving Symmetric Encryption (MOPSE) solution can be effectively merged by the cloud. The main merit of MEIM is that an individual only needs to issue one encrypted query to efficiently retrieve the PHRs of her interests, even if the indexes are encrypted under different symmetric keys. We prove that the query processing with MEIM for data user is n times faster than the tradition OPSE, where n denotes the number of data providers.

In wireless sensor networks, owing to the limited energy of the sensor node, it is very meaningful to propose a dynamic scheduling scheme with data management that reduces energy as soon as possible. However, traditional techniques treat data management as an isolated process on only selected individual nodes. In this article, we propose an aggressive data reduction architecture, which is based on error control within sensor segments and integrates three parallel dynamic control mechanisms. We demonstrate that this architecture not only achieves energy savings but also guarantees the data accuracy specified by the application. Furthermore, based on this architecture, we propose two implementations. The experimental results show that both implementations can raise the energy savings while keeping the error at an predefined and acceptable level. We observed that, compared with the basic implementation, the enhancement implementation achieves a relatively higher data accuracy. Moreover, the enhancement implementation is more suitable for the harsh environmental monitoring applications. Further, when both implementations achieve the same accuracy, the enhancement implementation saves more energy. Extensive experiments on realistic historical soil temperature data confirm the efficacy and efficiency of two implementations.

With its reprogrammability, low design cost, and increasing capacity, field-programmable gate array (FPGA) has become a popular design platform and a target for intellectual property (IP) infringement. Currently available IP protection solutions are usually limited to protect single FPGA configurations and require permanent secret key storage in the FPGA. In addition, they cannot provide a commercially popular pay-per-device licensing solution. In this paper, we propose a novel IP protection mechanism to restrict IP's execution only on specific FPGA devices in order to efficiently protect IPs from being cloned, copied, or used with unauthorized integration. This mechanism can also enforce the pay-per-device licensing, which enables the system developers to purchase IPs from the core vendors at the low price based on usage instead of paying the expensive unlimited IP license fees. In our proposed binding-based mechanism, FPGA vendors embed into each enrolled FPGA device with a physical unclonable function (PUF) customized for FPGAs; IP vendors embed augmented finite-state machines (FSM) into the original IPs such that the FSM can be activated by the PUF responses from the FPGA device. We propose protocols to lock and unlock FPGA IPs, demonstrate how PUF can be embedded onto FPGA devices, and analyze the security vulnerabilities of our PUF-FSM binding method. We implement a 128-bit delay-based PUF on 28-nm FPGAs with only 258 RAM-lookup tables and 256 flipflops. The PUF responses are unique and reliable against environment changes. We also synthesize a variety of FSM benchmark circuits. On large benchmarks, the average timing overhead is 0.64% and power overhead in 0.01%.

The FPGA replay attack, where an attacker downgrades an FPGA-based system to the previous version with known vulnerabilities, has become a serious security and privacy concern for FPGA design. Current FPGA intellectual property (IP) protection mechanisms target the protection of FPGA configuration bitstreams by watermarking or encryption or binding. However, these mechanisms fail to prevent replay attacks. In this article, based on a recently reported PUF-FSM binding method that protects the usage of configuration bitstreams, we propose to reconfigure both the physical unclonable functions (PUFs) and the locking scheme of the finite state machine (FSM) in order to defeat the replay attack. We analyze the proposed scheme and demonstrate how replay attack would fail in attacking systems protected by the reconfigurable binding method. We implement two ways to build reconfigurable PUFs and propose two practical methods to reconfigure the locking scheme. Experimental results show that the two reconfigurable PUFs can generate significantly distinct responses with average reconfigurability of more than 40%. The reconfigurable locking schemes only incur a timing overhead less than 1%.

With the advent of cloud computing, it becomes increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve these data. For privacy concerns, secure searches over encrypted cloud data motivated several researches under the single owner model. However, most cloud servers in practice do not just serve one owner, instead, they support multiple owners to share the benefits brought by cloud servers. In this paper, we propose schemes to deal with secure ranked multi-keyword search in a multi-owner model. To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel Additive Order and Privacy Preserving Function family. Extensive experiments on real-world datasets confirm the efficacy and efficiency of our proposed schemes.

Physical unclonable function (PUF) is a promising hardware security primitive that can be applied to various security related areas. The ring oscillator (RO) PUF is one of the most popular PUFs that can generate the volatile key by comparing the frequency between ROs. Previous RO PUFs incur unacceptable hardware overheads to improve the reliability in order to eliminate the effect of environment factors. In this paper, we propose a frequency offset algorithm (FOA) to enhance the reliability and low the hardware overhead. The key idea is to make the frequency difference larger than a given threshold by offsetting the frequencies of RO pairs. Experimental results show that our proposed FOA method has the better reliability and lower hardware overhead than the temperature-aware cooperative (TAC). Especially, our proposed method can achieve the 100% utilization of ROs.

Cloud computing provides abundant benefits including easy access, decreased costs and flexible resource management. For privacy concerns, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Therefore, developing a secure search service over encrypted cloud data is of paramount importance. There are several researches concerned about this problem. However, all these schemes are based on a single cloud model which has the threat of single point of failure, loss and corruption of data, loss of availability and loss of privacy. In this paper, we explore the problem of secure distributed keyword search in a multi-cloud paradigm. We first define a distributed search model. Based on this model, we propose two schemes. In scheme_I, we propose to cross-store all encrypted file slices, keywords and keys. In scheme_II, we systematically construct a keyword distributing strategy and a file distributing strategy. Further, we extend both schemes with Shamir's secret schemes to achieve better availability and robustness. Extensive experiments on real-world datasets confirm the efficacy and efficiency of our schemes.

Video Surveillance has been widely used in business establishments. Since digital cameras everlastingly collect the video data, the volume of sampled data is extensively large, which is hard to be stored and managed locally. Outsourcing surveillance video data to the cloud can achieve cost saving and flexibility, but also will incur potential privacy leakage. In this paper, we utilize the Compressed Sensing (CS) technique for sampling and compressing, to achieve secure and efficient video surveillance in cloud computing. Firstly, we identify the known-plaintext attack in such environment. That is, given sufficient information about the original signal and corresponding CS measurements, the attacker is likely to calculate the measurement matrix. Then, we propose a Dynamic Compressive Sensing(DCS) scheme to resist such an attack. Specifically, we use a dynamic measurement matrix that is changeable over time to prevent the attackers from gaining sufficient information to calculate the measurement matrix. Furthermore, we allow the cloud to help users decode the non-reference frames without leaking any information, to take full advantage of the powerful computing. Experimental results show that the proposed scheme effectively protects the security of the surveillance video, and provides a good recovery quality for users to conduct further analysis.

Storage nodes in two-tiered sensor networks are responsible for storing sensor-collected data and processing the sink-issued queries. Therefore, storage nodes are vulnerable to attack because of their importance. In this paper, we propose a privacy and integrity preserving protocol called SSQ, which is able to prevent compromised storage nodes from leaking sensitive data and allows the sink to detect the misbehaviors of compromised storage nodes. For privacy preserving, a size-limited bucketing technique is proposed to mix the data in a range, and a prefix membership verification technique based on Bloom filters is developed to perform skyline queries on encrypted data items. For integrity preserving, a Merkle hash tree-based technique is investigated to prevent compromised storage nodes from tampering and dropping data. Detailed performance evaluations confirm the high efficacy and efficiency of SSQ. Copyright © 2013 John Wiley & Sons, Ltd.

Physical Unclonable Function (PUF) makes use of the uncontrollable process variations during the production of IC to generate a unique signature for each IC. It has a wide application in security such as FPGA Intellectual Property (IP) protection, key generation and digital rights management. Ring Oscillator (RO) based PUF and Arbiter-based PUF are the most popular PUFs, but they are not specially designed for FPGA. RO-based PUF incurs high resource overhead while obtaining less challenge-response pairs, and requires ``hard macros'' to implement on FPGA. The arbiter-based PUF brings low resource overhead, but its structure is hard to be mapped on FPGA. Anderson'PUF can address these weaknesses of current Arbiter-based and RO-based PUFs. However, it cannot be directly implemented on the new generation FPGAs, and therefore it has the scalability issue. In order to address these problems, this paper presents a delay-based PUF using the intrinsic structure of FPGA (look-up table and multiplexer). The proposed delay-based PUF is completely realized on 28nm FPGAs. The experimental results show its high uniqueness and reliability. Moreover, we test the proposed PUF in the high temperature, and the results show its availability. Finally, the prospect of the proposed PUF in the FPGA IP protection is discussed.

The continuous growth in both capability and capacity for FPGA now requires significant resources invested in the hardware design, which results in two classes of main security issues: 1) the unauthorized use and piracy attacks including cloning, reverse engineering, tampering etc. 2) the licensing issue. Binding hardware IPs (HW-IPs) to specific FPGA devices can efficiently resolve these problems. However, previous binding techniques are all based on encryption and hence have three main drawbacks: 1) encryption-based proposals in commercial are limited to protect the single large FPGA configuration, 2) many encryption-based proposals depend on a trusted third party to involve the licensing protocol, and 3) the encryption-based binding methods use costly mechanisms such as secure ROM or flash memory to store FPGA specific cryptographic keys, which is not only expensive but also vulnerable to side-channel attacks, and the management and transport of secret keys became a practical issue. In this work, we propose a PUF-FSM binding technique completely different from the traditional encryption-based methods to address these shortcomings.

In this paper we propose a novel binding mechanism that can protect FPGA IP from being cloned, tampered, or misused; and facilitate the pay-per-use licensing to limit the FPGA IP's execution to specific FPGA devices only. In this mechanism, the FPGA vendors will provide each enrolled device with a Physical Unclonable Function (PUF) that can be deployed securely during fabrication process. The core vendor will embed an augmented Finite State Machine (FSM) into the original FSM structure of the hardware IP (HW-IP) to react on the PUF response to a given challenge. The proposed binding method does not need any Trusted Third Party (TTP) or block cipher for key management and exchange. We analyze several known attacks to hardware IP and show that our method is secure against these attacks. Experimental results on MCNC benchmarks show that the proposed method incurs small design overhead in terms of area, power and delay.

Two-tiered wireless sensor networks offer good scalability, efficient power usage, and space saving. However, storage nodes are more attractive to attackers than sensors because they store sensor collected data and processing sink issued queries. A compromised storage node not only reveals sensor collected data, but also may reply incomplete or wrong query results. In this paper, we propose QuerySec, a protocol that enables storage nodes to process queries correctly while prevents them from revealing both data from sensors and queries from the sink. To protect privacy, we propose an order preserving function-based scheme to encode both sensor collected data and sink issued queries, which allows storage nodes to process queries correctly without knowing the actual values of both data and queries. To preserve integrity, we proposed a link watermarking scheme, where data items are formed into a link by the watermarks embedded in them so that any deletion in query results can be detected.

Wireless sensor networks are often deployed in diverse application specific contexts and one unifying view is to treat them essentially as distributed databases. The simplest mechanism to obtain information from this kind of database is to flood queries for named data within the network and obtain the relevant responses from sources. However, if the queries are issued for replicated data, the simple approach can be highly inefficient. As sensor networks are uniquely characterized by limited energy availability and low memory, alternative strategies need to be examined for this kind of queries. A novel query processing approach using distributed Multiple Ant Colonies algorithm with positive interaction is presented in this paper, in which ants adjust individual behavior via cooperation to make colony behavior intelligent, demanding merely local information to find named data efficiently and determine the number and allocation of event replicas adaptively. Theoretically and experimentally, the results clearly show that the proposed protocol is more flexible and energy-efficient than existing algorithms.

Key pre-distribution schemes based on regular network such as hypercube have several advantages. Examples are lower storage cost and ability to find a proper key path more quickly. However, the probability to establish direct keys is fairly low. Based on available weak connectivity of hypercube, the framework of local leveled connectivity model is presented in key-sharing graph, and also node's localized searching algorithm is proposed. Experiments show that presented schemes do improve node's average degree in pairwise key-sharing graph.

In unattended or hostile environment like battlefield, sensor nodes may be compromised. If one node is compromised, group key which is shared by all the group members should be rekeyed in real-time and the compromised nodes should be revoked as soon as possible. As there is no such node that can always be trusted in sensor network, a distributed group rekeying scheme with speedy compromised node revocation is proposed in this paper. Based on a novelly constructed authentication polynomial, group rekeying is initiated by local network collaboratively in our scheme. Compared with the existing schemes, the proposed scheme can not only rekey in real-time, but reduce more communication overheads and provide a high level of security at the same time.

In this paper, we propose a pairwise key distribution scheme based on a novel interconnection graph termed Hierarchical Hypercube. Motivated by the fact that sensor nodes are often deployed in groups (for example, dropped from an airplane at different locations) and hence the whole network is composed of multiple such groups, we design Hierarchical Hypercube as a two layer topology, where each group is modeled by an inner hypercube and connections between the groups are modeled using an outer hypercube. we propose a topology termed Hierarchy Hypercube. While retaining the desirable properties already shown by existing pairwise schemes, by using Hierarchical Hypercube, direct communication from a node to any other nodes is not required, either within a group or among groups, and hence this topology can effectively and realistically reflect the connectivity of the physical sensor network deployed in groups. Furthermore, we propose key pre-distribution scheme based on this novel topology and show that the new scheme possesses high probability of direct key establishment, low memory overhead, and resilience in the presence of broken communication links and compromised nodes, and thus still retain the desirable properties even when the ideal logical connection are distorted in the real deployment.

It's essential on algorithm design of designated confirmer signatures to construct proofs satisfying security requirements such as unforgetablility, non-transferability, invisibility and zero-knowledge. A designated confirmer signature protocol (RSA-DCSV) is proposed, based on RSA encryption and signature schemes in forms of RSA extended modular computations. Proofs for a designated verifier are considered. Security analysis on RSA-DCSV is also addressed.