Three Branches of Negative Representation of Information: A Survey

Luo, Wenjian; Liu, Ran; Jiang, Hao; Zhao, Dongdong; Wu, Lisheng

doi:10.1109/tetci.2018.2829907

Cited by 23 publications

(7 citation statements)

References 100 publications

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“…Therefore, it is reasonable to employ the KHidden-M algorithm in the proposed protocol (Zhao & Luo, 2013) to solve the above problem. Luo et al (2018) summarized more applications of NDBs, e.g., authentication, secure multiparty computation, information hiding, privacy-preserving data mining and data publication.…”

Section: Discussionmentioning

confidence: 99%

Generating Random SAT Instances: Multiple Solutions could be Predefined and Deeply Hidden

Zhao

Liao²,

Luo³

et al. 2023

jair

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The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances.

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Section: Discussionmentioning

confidence: 99%

Generating Random SAT Instances: Multiple Solutions could be Predefined and Deeply Hidden

Zhao

Liao²,

Luo³

et al. 2023

jair

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“…The NRI feature has become a standalone topic that is inspired by the discrimination of the self and non-self-mechanism in the BIS and stores the complement information rather than the information itself. This emerging topic contains three main elementary branches which are the NSA, Negative Databases, and Negative Surveys [161]. Basically, NRI is used as a technique for security and privacy.…”

Section: Author and Year Ais Algorithm Application Domainmentioning

confidence: 99%

Towards a Hybrid Immune Algorithm Based on Danger Theory for Database Security

Said

2020

IEEE Access

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In Databases, the most prevalent cause of data breaches comes from insiders who misuse their account privileges. Due to the difficulty of discovering such breaches, an adaptive, accurate, and proactive database security strategy is required. Intrusion detection systems are utilized to detect, as fast as possible, user's account privilege misuse when a prevention mechanism has failed to address such breaches. In order to address the foremost deficiencies of intrusion detection systems, artificial immune systems are used to tackle these defects. The dynamic and more complex nature of cybersecurity, as well as the high false positive rate and high false negative percentage in current intrusion detection systems, are examples of such deficiency. In this paper, we propose an adaptable efficient database intrusion detection algorithm based on a combination of the Danger Theory model and the Negative Selection algorithm from artificial immune system mechanisms. Experimental results for the implementation of the proposed algorithm provide a self-learning mechanism for achieving high detection coverage with a low false positive rate by using the signature of previously detected intrusions as detectors for the future detection process. The proposed algorithm can enhance detecting insider threats and eliminate data breaches by protecting confidentiality, ensuring integrity, and maintaining availability. To give an integrated picture, a comprehensive and informative survey for the different research directions that are related to the proposed algorithm is performed.

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“…The negative representation of information is a branch of the artificial immune system [12], which has been employed to collect and hide sensitive information and formed two new branches called negative survey (collect sensitive information) [13] and negative database (hide sensitive information) [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Jiang,

Liao,

Zhao

et al. 2024

CMES

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Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private. Moreover, a negative database-based method is proposed to hide node attributes, so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes, which is crucial to the analysis of social networks. To evaluate the performance of the AttNetNRI, empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks. The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts. The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

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Three Branches of Negative Representation of Information: A Survey

Cited by 23 publications

References 100 publications

Generating Random SAT Instances: Multiple Solutions could be Predefined and Deeply Hidden

Generating Random SAT Instances: Multiple Solutions could be Predefined and Deeply Hidden

Towards a Hybrid Immune Algorithm Based on Danger Theory for Database Security

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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