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 ε-differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.

In this paper, a local spatio-temporal descriptor, namely, volume local binary count (VLBC), is proposed for the representation and recognition of dynamic texture. This descriptor, which is similar in spirit to volume local binary pattern (VLBP), extracts histograms of thresholded local spatio-temporal 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 2D face spoofing detection. As an effective spatio-temporal 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 anti-spoofing databases: Print-Attack, Replay-Attack and CAS Face Anti-Spoofing. The experimental results demonstrate the effectiveness of CVLBC for 2D face spoofing detection.

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.

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.

Trending topic recommendation (TTR) has become a popular social service for social users to obtain interesting topics based on public social contents. 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 paper, we propose a novel local DP-based trending topic recommendation (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 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 TDS 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 datasets show that proposed LDPTR gets high data utility while ensuring high-level privacy.