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

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 paradigmin 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.

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

Why Is nice and Adj So Much More Frequent than Adj and nice?——From the Perspective of Humans’ Social and Limited-Processing-Capacity Attributes

This paper investigates the phenomenon of imbalance between the frequencies of the nice and Adj and Adj and nice patterns from the perspective of humans’ social and limited-processing-capacity attributes. Humans’ social attribute requires that language users stay informative with minimal effort in communication, resulting in the from-the-least-to-the-most-informative information organization in discourse. Their limited-processing-capacity attribute requires that they resort to the production biases of "easy first" and "plan reuse" in order to achieve communicative efficiency in real-time production. The analysis of the occurrences of the nice and Adj pattern and native speakers’ judgment of the degree of informativeness of nice in these occurrences suggest that nice is largely delexicalized. Such delexicalization makes nice and Adj consistent with the information organization and allows language users to stay informative with the use of the pattern, thus in line with the social attribute, but not Adj and nice. In the meantime, nice is not only highly frequent but also conceptually salient when it comes to the positive property(Panther & Thornburg, 2009), making nice and Adj easier to produce and more likely to be reused than Adj and nice, thus in line with the limited-processing-capacity attribute. The analysis of the unbalanced frequency of the two patterns suggests that human attributes should be considered when studying language form, and this should offer insights into English learning.

Discussion on Earthquake Forecasting and Early Warning

Through analysis of natural and social attributes of earthquake forecasting,the relationship between the natural and social attributes of earthquake forecasting(early warning)has been discussed.Regarding the natural attributes of earthquake forecasting,it only attempts to forecast the magnitude,location and occurrence time of future earthquake based on the analysis of observational data and relevant theories and taking into consideration the present understanding of seismogeny and earthquake generation.It need not consider the consequences an earthquake forecast involves,and its purpose is to check out the level of scientific understanding of earthquakes.In respect of the social aspect of earthquake forecasting,people also focus on the consequence that the forecasting involves,in addition to its natural aspect,such as the uncertainty of earthquake prediction itself,the impact of earthquake prediction,and the earthquake resistant capability of structures(buildings),lifeline works,etc.In a word,it highlights the risk of earthquake forecasting and tries to mitigate the earthquake hazard as much as possible.In this paper,the authors also discuss the scientific and social challenges faced in earthquake prediction and analyze preliminarily the meanings and content of earthquake early warning.