Visible to the public Biblio

Filters: Author is Zhang, Kai  [Clear All Filters]
2020-01-20
Ren, Zhengwei, Zha, Xianye, Zhang, Kai, Liu, Jing, Zhao, Heng.  2019.  Lightweight Protection of User Identity Privacy Based on Zero-knowledge Proof. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). :2549–2554.
A number of solutions have been proposed to tackle the user privacy-preserving issue. Most of existing schemes, however, focus on methodology and techniques from the perspective of data processing. In this paper, we propose a lightweight privacy-preserving scheme for user identity from the perspective of data user and applied cryptography. The basic idea is to break the association relationships between User identity and his behaviors and ensure that User can access data or services as usual while the real identity will not be revealed. To this end, an interactive zero-knowledge proof protocol of identity is executed between CSP and User. Besides, a trusted third-party is introduced to manage user information, help CSP to validate User identity and establish secure channel between CSP and User via random shared key. After passing identity validation, User can log into cloud platform as usual without changing existing business process using random temporary account and password generated by CSP and sent to User by the secure channel which can further obscure the association relationships between identity and behaviors. Formal security analysis and theoretic and experimental evaluations are conducted, showing that the proposal is efficient and practical.
2019-05-01
Yu, Wenchao, Cheng, Wei, Aggarwal, Charu C., Zhang, Kai, Chen, Haifeng, Wang, Wei.  2018.  NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. :2672-2681.

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors'' deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3) flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.

2018-08-23
Zhang, Kai, Liu, Chuanren, Zhang, Jie, Xiong, Hui, Xing, Eric, Ye, Jieping.  2017.  Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :615–623.
Matrix sketching is aimed at finding compact representations of a matrix while simultaneously preserving most of its properties, which is a fundamental building block in modern scientific computing. Randomized algorithms represent state-of-the-art and have attracted huge interest from the fields of machine learning, data mining, and theoretic computer science. However, it still requires the use of the entire input matrix in producing desired factorizations, which can be a major computational and memory bottleneck in truly large problems. In this paper, we uncover an interesting theoretic connection between matrix low-rank decomposition and lossy signal compression, based on which a cascaded compression sampling framework is devised to approximate an m-by-n matrix in only O(m+n) time and space. Indeed, the proposed method accesses only a small number of matrix rows and columns, which significantly improves the memory footprint. Meanwhile, by sequentially teaming two rounds of approximation procedures and upgrading the sampling strategy from a uniform probability to more sophisticated, encoding-orientated sampling, significant algorithmic boosting is achieved to uncover more granular structures in the data. Empirical results on a wide spectrum of real-world, large-scale matrices show that by taking only linear time and space, the accuracy of our method rivals those state-of-the-art randomized algorithms consuming a quadratic, O(mn), amount of resources.
2017-10-10
Zhang, Kai, Gong, Junqing, Tang, Shaohua, Chen, Jie, Li, Xiangxue, Qian, Haifeng, Cao, Zhenfu.  2016.  Practical and Efficient Attribute-Based Encryption with Constant-Size Ciphertexts in Outsourced Verifiable Computation. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :269–279.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.

2017-09-15
Cheng, Wei, Zhang, Kai, Chen, Haifeng, Jiang, Guofei, Chen, Zhengzhang, Wang, Wei.  2016.  Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :805–814.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

2017-08-22
Cheng, Wei, Zhang, Kai, Chen, Haifeng, Jiang, Guofei, Chen, Zhengzhang, Wang, Wei.  2016.  Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :805–814.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

2017-08-18
Zhang, Kai, Gong, Junqing, Tang, Shaohua, Chen, Jie, Li, Xiangxue, Qian, Haifeng, Cao, Zhenfu.  2016.  Practical and Efficient Attribute-Based Encryption with Constant-Size Ciphertexts in Outsourced Verifiable Computation. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :269–279.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.