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Symmetric Searchable Encryption(SSE) is deemed to tackle the privacy issue as well as the operability and confidentiality in data outsourcing. However, most SSE schemes assume that the cloud is honest but curious. This assumption is not always applicable. And even if some schemes supported verification, integrity or freshness checking in a malicious cloud, but the performance and security functionalities are not fully exploited. In this paper, we propose an efficient SSE scheme based on B+-Tree and Counting Bloom Filter (CBF) which supports secure verification, dynamic updating, and multiuser queries. Comparing with the previous state of the arts, we design the new data structure CBF to support dynamic updating and boost verification. We also leverage the timestamp mechanism in the scheme to prevent the malicious cloud from launching a replay attack. The new designed CBF is like a front-engine to save user s cost for query and verification. And it can achieve more efficient query and verification with negligible false positive when there is no value matching the queried keyword. The CBF supports efficient dynamic updating by combining Bloom Filter with a one-dimensional array that provides the counting capability. Furthermore, we design the authenticator for CBF. We adopt B+-Tree for it is widely used in many database engines and file systems. We also give a brief security proof of our scheme. Then we provide a detailed performance analysis. Finally, we evaluate our scheme through comprehensive experiments. The results are consistent with our analysis and show that our scheme is secure, and more efficient compared with the previous schemes with the same functionalities. The average performance can be improved by about 20% for both the cloud servers and users when the missing rate of the searching keywords is 20%. And the higher the missing rate is, the more the performance can be improved.

To achieve the tamper-proof, reliability and traceability of transactions in a trustless environment, the blockchain requires each peer node to store the whole global ledger. However, as transactions keep increasing over time, the storage cost of each node increases. In addition, many schemes have been proposed to boost rapid transactions which will even lead transactions to grow explosively. The problem of storage is becoming one challenge of blockchain since the storage overhead of each node increase rapidly. Reducing the storage overhead of each participant is very urgent and worthy. In this paper, we present GCBlock: a grouping overlay network storage scheme for the blockchain which can reduce the storage overhead of nodes and cut the whole storage cost of the blockchain greatly while keeping the underlying protocols. In our scheme, we try to group the nodes according to their physical fuzzy distance to reduce the overall delay when tracing. We set rules of autonomous check to deal with evil behavior within the group. To further enhance the stability of our scheme, we propose the transcript fractional repetition code which is newly constructed based on the fractional repetition code to encode data. Finally, we make a comprehensive evaluation of GCBlock and the results show that it is workable and reasonable.INDEX TERMS Blockchain, distributed storage, overlay network, network coding.

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Abstract:User targeting via behavioral analysis is becoming increasingly prevalent in online messaging services. By taking into account users' behavior information such as geographic locations, purchase behaviors, and search histories, vendors can deliver messages to users who are more likely to have a strong preference. For example, advertisers can rely on some ad-network for distributing ads to targeted users. However, collecting such personal information for accurate targeting raises severe privacy concerns. In order to incentivize users to participate in such behavioral targeting systems, addressing the privacy concerns becomes of paramount importance. We provide a survey of privacy-preserving user targeting. We present the architectures of user targeting, the security threats faced by user targeting, and existing approaches to privacy-preserving user targeting. Some IXWXUH UHVHDUFK GLUHFWLRQV DUH DOVR LGHQWL¿HG

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At present, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and has shined in many data mining competitions and real-world applications for its salient results on classification, ranking, prediction, etc. Federated learning which aims to mitigate privacy risks and costs, enables many entities to keep data locally and train a model collaboratively under an orchestration service. However, most of the existing systems often fail to make an excellent trade-off between accuracy and communication. In addition, they overlook an important aspect: fairness such as performance gains from different parties’ datasets. In this paper, we propose a novel federated GBDT scheme based on the blockchain which can achieve constant communication overhead and good model performance and quantify the contribution of each party. Specifically, we replace the tree-based communication scheme with the pure gradient-based scheme and compress the intermediate gradient information to a limit to achieve good model performance and constant communication overhead in skewed datasets. On the other hand, we introduce a novel contribution allocation scheme named split Shapley value, which can quantify the contribution of each party with a limited gradient update and provide a basis for monetary reward. Finally, we combine the quantification mechanism with blockchain organically and implement a closed-loop federated GBDT system FGBDT-Chain in a permissioned blockchain environment and conduct a comprehensive experiment on public datasets. The experimental results show that FGBDT-Chain achieves a good trade-off between accuracy, communication overhead, fairness, and security under large-scale skewed datasets.