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As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as "public accessible", which can be accessed by the public.

A fine-grained provenance-based access control policy model is proposed in this paper, in order to improve the express performance of existing model. This method employs provenance as conditions to determine whether a piece of data can be accessed because historical operations performed on data could reveal clues about its sensitivity and vulnerability. Particularly, our proposed work provides a four-valued decision set which allows showing status to match a restriction particularly. This framework consists of target policy, access control policy, and policy algebras. With the complete definition and algebra system construction, a practical fine-grained access control policy model is developed.

Even though the idea of partitioning provenance graphs for access control was previously proposed, employing segments of the provenance DAG for fine-grained access control to provenance data has not been thoroughly explored. Hence, we take segments of a provenance graph, based on the extended OPM, and defined use a variant of regular expressions, and utilize them in our fine-grained access control language. It can not only return partial graphs to answer access requests but also introduce segments as restrictions in order to screen targeted data.

Face detection has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs). Its central issue in recent years is how to improve the detection performance of tiny faces. To this end, many recent works propose some specific strategies, redesign the architecture and introduce new loss functions for tiny object detection. In this report, we start from the popular one-stage RetinaNet approach and apply some recent tricks to obtain a high performance face detector. Specifically, we apply the Intersection over Union (IoU) loss function for regression, employ the two-step classification and regression for detection, revisit the data augmentation based on data-anchor-sampling for training, utilize the max-out operation for classification and use the multi-scale testing strategy for inference. As a consequence, the proposed face detection method achieves state-of-the-art performance on the most popular and challenging face detection benchmark WIDER FACE dataset., 9 pages, 3 figures, technical report

It is a crucial mechanism of access control to determine that data can only be accessed for allowed purposes. To achieve this mechanism, we propose purpose-based access policies in this paper. Different from provenance-based policies that determine if a piece of data can be accessed or not, purpose-based access policies determines for what purposes can data be accessed. Particularly, the purposes can be classified as different sensitivity levels. For the first time, We tailor policy algebras to include internal and external policy operators for hierarchical purposes, in order to merge purpose sets generated by individual policies. We also created external policy algebras to merge policies from multi-parties. With different types' testing experiments, our model is proved to be feasible and practical.

To deal with various datasets over different complexity, this paper presents an self-adaptive learning model that combines the proposed Dynamic Connected Neural Decision Networks (DNDN) and a new pruning method--Dynamic Soft Pruning (DSP). DNDN is a combination of random forests and deep neural networks that enjoys both the advantages of strong classification capability of tree-like structure and representation learning capability of network structure. Based on Deep Neural Decision Forests (DNDF), this paper adopts an end-to-end training approach by representing the classification distribution with multiple randomly initialized softmax layers, which further allows an ensemble of multiple random forests attached to layers of neural network with different depth. We also propose a soft pruning method DSP to reduce the redundant connections of the network adaptively to avoid over-fitting simple dataset. The model demonstrates no performance loss compared with unpruned models and even higher robustness over different data and feature distribution. Extensive experiments on different datasets demonstrate the superiority of the proposed model over other popular algorithms in solving classification tasks.

Data security and privacy is an important but challenging problem in cloud computing. One of the security concerns from cloud users is how to efficiently verify the integrity of their data stored on the cloud server. Third Party Auditing (TPA) is a new technique proposed in recent years to achieve this goal. In a recent paper (IEEE Transactions on Computers 62(2): 362-375 (2013)), Wang et al. proposed a highly efficient and scalable TPA protocol and also a Zero Knowledge Public Auditing protocol which can prevent offline guessing attacks. However, in this paper, we point out several security weaknesses in Wang et al's protocols: first, we show that an attacker can arbitrarily modify the cloud data without being detected by the auditor in the integrity checking process, and the attacker can achieve this goal even without knowing the content of the cloud data or any verification metadata maintained by the cloud server; secondly, we show that the Zero Knowledge Public Auditing protocol cannot achieve its design goal, that is to prevent offline guessing attacks., Comment: arXiv admin note: text overlap with arXiv:1912.00446

With the wide application of cloud storage, cloud security has become a crucial concern. Related works have addressed security issues such as data confidentiality and integrity, which ensure that the remotely stored data are well maintained by the cloud. However, how to define zero-knowledge proof algorithms for stored data integrity check has not been formally defined and investigated. We believe that it is important that the cloud server is unable to reveal any useful information about the stored data. In this paper, we introduce a novel definition of data privacy for integrity checks, which describes very high security of a zero-knowledge proof. We found that all other existing remote integrity proofs do not capture this feature. We provide a comprehensive study of data privacy and an integrity check algorithm that captures data integrity, confidentiality, privacy, and soundness.

In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases. However, the predictions are neither robust nor adequate enough since they only capture a few conditional distributions instead of the whole distribution, especially for small datasets. To address this problem, we proposed arbitrary quantile modeling to regulate the prediction, which achieved better performance compared to traditional loss functions. More specifically, a new distribution regression method, Deep Distribution Regression (DDR), is proposed to estimate arbitrary quantiles of the response variable. Our DDR method consists of two models: a Q model, which predicts the corresponding value for arbitrary quantile, and an F model, which predicts the corresponding quantile for arbitrary value. Furthermore, the duality between Q and F models enables us to design a novel loss function for joint training and perform a dual inference mechanism. Our experiments demonstrate that our DDR-joint and DDR-disjoint methods outperform previous methods such as AdaBoost, random forest, LightGBM, and neural networks both in terms of mean and quantile prediction.