基于聚类的动态社交网络隐私保护方法

doi:10.11959/j.issn.1000-436x.2015290

Abstract

Abstract:

Due to the dynamic characteristics of the social network graph structure,an effective dynamic privacy preserving method was needed.To solve the problems of the existing dynamic privacy preservation methods,such as attacker’s too little background knowledge and the low adaptability to the dynamic characteristics of graph structure,a clustering-based dynamic privacy preservation method was provided.The analysis shows that the proposed method can resist many kinds of background knowledge attacks and has good adaptability to the dynamic characteristics of the social network graph structure.

Key words: dynamic social networks, privacy preserving, clustering, information loss degree, anonymization rate

Yong-hao GU,Jiu-chuan LIN,Da GUO. Clustering-based dynamic privacy preserving method for social networks[J]. Journal on Communications, 2015, 36(Z1): 126-130.

Figures/Tables 3

References 21

[1]	付艳艳, 张敏, 冯登国 , 等. 基于节点分割的社交网络属性隐私保护[J]. 软件学报, 2014,25(4): 768-780. FU Y Y , ZHANG M , FENG D G，et al . Attribute privacy preservation in social networks based on node anatomy[J]. Journal of Software, 2014,25(4): 768-780.
[2]	刘向宇, 王斌, 杨晓春 . 社会网络数据发布隐私保护技术综述[J]. 软件学报, 2014,25(3): 576-590. LIU X Y , WANG B , YANG X C . Survey on privacy preserving techniques for publishing social network data[J]. Journal of Software, 2014,25(3): 576-590.
[3]	LIU X , YANG X . Protecting sensitive relationships against inference attacks in social networks[A]. Proc of the 17th Int’l Conf on Database Systems for Advanced Applications[C]. 2012. 335-350.
[4]	GAO J , XU J Y , JIN R . Neighborhood-privacy protected shortest distance computing in cloud[A]. Proc of the 2011 ACM SIGMOD Int’l Conf on Management of Data[C]. 2011. 409-420.
[5]	BHAGAT S , CORMODE G , SRIVASTAVA D . Privacy in dynamic social networks[A]. Proceedings of the 19th International Conference on World Wide Web[C]. ACM, 2010. 1059-1060.
[6]	YING X , WU X , YING X . Randomizing social networks:a spectrum preserving approach[J]. SDM, 2008: 739-750.
[7]	THOMPSON B , YAO D . The union-split algorithm and cluster-based anonymization of social networks[A]. Proceedings of the 4th International Symposium on Information,Computer,and Communications Security[C]. 2009. 218-227.
[8]	ZHELEVA E , GETOOR. L . Preserving the privacy of sensitive relationships in graph data[A]. Privacy,Security,and Trust in KDD[C]. 2008. 153-171.
[9]	ANDERSEN S K , JUDEA P . Probabilistic reasoning in intelligent systems:networks of plausible inference[J]. Artificial Intelligence, 1991,48(91): 117-124.
[10]	CAMPAN A , TRUTA T M . A clustering approach for data and structural anonymity in social networks[J]. Privacy,Security,and Trust in KDD Workshop (PinKDD), 2008. 33-54.
[11]	CORMODE G , SRIVASTAVA D , Yu T , et al. Anonymizing bipartite graph data using safe groupings[J]. VLDB Journal, 2010,19(1): 115-139.
[12]	VISWANATH B ， MISLOVE A ， CHA M , et al. On the evolution of user interaction in facebook[A]. Proceedings of the 2nd ACM Workshop on Online Social Networks[C]. 2009. 37-42.
[13]	CHENG J , FU AWC , LIU J . K-isomorphism:privacy preserving network publication against structural attacks[A]. Proc of the 2010 ACM SIGMOD Int’l Conf on Management of Data[C]. 2010. 459-470.
[14]	ZOU L , CHEN L , OZSU MT . K-automorphism:a general framework for privacy preserving network publication[A]. Proc of the 35th International Conference on Very Large Databases[C]. 2009. 946-957.
[15]	张晓琳, 李玉峰, 王颖 . 动态社会网络隐私保护方法研究[J]. 计算机应用研究, 2012,29(4): 1434-1437. ZHANG X L , LI Y F , WANG Y . Research on privacy preserving method for dynamic social network[J]. Application Research of Computers, 2012,29(4): 1434-1437.
[16]	KARWA V , SOFYA R , SMITH A , et al. Private analysis of graph structure[A]. Proc of the 37th International Conference on Very Large Databases[C]. 2011. 1147-1157.
[17]	CHEN S , ZHOU S . Recursive mechanism:towards node differential privacy and unrestricted joins[A]. Proc of the International Conference on Management of Data[C]. 2013. 653-664.
[18]	CHEN R , FANG B C M ， PHILIP S Y , et al. Correlated network data publication via differential privacy[J]. The VLDB Journal, 2014,23(4): 653-676
[19]	张伟, 王旭然, 王珏 , 等. 基于 k-邻域同构的动态社会网络隐私保护方法[J]. 南京邮电大学学报(自然科学版), 2014,34(5): 9-16. ZHANG W , WANG X R , WANG J , et al. Privacy preservation in dynamic social networks based on k-neighborhood isomorphism[J]. Journal of Nanjing University of Posts and Telecommunications(Natural Science), 2014,34(5): 9-16.
[20]	孙浩月 . 防止图结构攻击的社会网络隐私保护技术研究[D]. 东北大学, 2011. SUN H Y . Research on the techniques of social network privacy preserving against graph structural attacks[D]. Northeastern University, 2011.
[21]	AGGRAWAL G , FEDER T , KENTHAPADI K , et al. Achieving Anonymity via Clustering[A]. Proceedings of the 25th ACM SIGACT-SIGMODSIGART Symposium on Principles of Database Systems[C]. 2006,6(3): 153-162.

Metrics

Recommended 0

No Suggested Reading articles found!

Clustering-based dynamic privacy preserving method for social networks

RichHTML

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 3

References 21

Related Articles 15

Metrics

Recommended 0

[1]	Yu DONG, Youpeng ZHANG. Conflict evidence combination method based on clustering weighting [J]. Journal on Communications, 2023, 44(3): 157-163.
[2]	Jing ZHAO, Jun LI, Chun LONG, Wei WAN, Jinxia WEI, Kai CHEN. Unsupervised detection method of RoQ covert attacks based on multilayer features [J]. Journal on Communications, 2022, 43(9): 224-239.
[3]	Ao LI, Cong FENG, Yutong NIU, Shibiao XU, Yingtao ZHANG, Guanglu SUN. Multiview clustering method for view-unaligned data [J]. Journal on Communications, 2022, 43(7): 143-152.
[4]	Rong QIAN, Jianting XU, Kejun ZHANG, Hongyu DONG, Fangyuan XING. Research on HMM based link prediction method in heterogeneous network [J]. Journal on Communications, 2022, 43(5): 214-225.
[5]	Yimin MAO, Dejin GAN, Liefa LIAO, Zhigang CHEN. Parallel division clustering algorithm based on Spark framework and ASPSO [J]. Journal on Communications, 2022, 43(3): 148-163.
[6]	Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU. Outlier detection algorithm based on fast density peak clustering outlier factor [J]. Journal on Communications, 2022, 43(10): 186-195.
[7]	Huiqin WANG, Wenbin HOU, Qingbin PENG, Minghua CAO, Rui HUANG, Ling LIU. Step-by-step classification detection algorithm of SPPM based on K-means clustering [J]. Journal on Communications, 2022, 43(1): 161-171.
[8]	Lichuan MA, Jiayi PENG, Qingqi PEI, Haojin ZHU. Efficient privacy-preserving decision tree classification protocol [J]. Journal on Communications, 2021, 42(8): 80-89.
[9]	Qiang HU, Jiaji SHEN, Guanghui JING, Junwei DU. Service clustering method based on description context feature words and improved GSDMM model [J]. Journal on Communications, 2021, 42(8): 176-187.
[10]	Chunfu JIA, Ruiqi LI, Yafei WANG. Privacy protection scheme of DBSCAN clustering based on homomorphic encryption [J]. Journal on Communications, 2021, 42(2): 1-11.
[11]	Liu LIU, Jianhua ZHANG, Yuanyuan FAN, Li YU, Jiachi ZHANG. Survey of application of machine learning in wireless channel modeling [J]. Journal on Communications, 2021, 42(2): 134-153.
[12]	Ao LI, Zhuo WANG, Xiaoyang YU, Deyun CHEN, Yingtao ZHANG, Guanglu SUN. Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning [J]. Journal on Communications, 2021, 42(11): 193-204.
[13]	Ruizhang HUANG,Ruina BAI,Yanping CHEN,Yongbin QIN,Xinyu CHENG,Youliang TIAN. CMDC:an iterative algorithm for complementary multi-view document clustering [J]. Journal on Communications, 2020, 41(8): 155-164.
[14]	Feiyue QIU,Bowen CHEN,Tieming CHEN,Guodao ZHANG. Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization [J]. Journal on Communications, 2020, 41(5): 84-95.
[15]	Xiaofei WANG,Fankui HU,Shuo HUANG. Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering [J]. Journal on Communications, 2020, 41(5): 120-129.