Publishing Graph Node Strength Histogram with Edge Differential Privacy

Protecting the private graph data while releasing accurate estimate of the data is one of the most challenging problems in data privacy. Node strength combines the topological information with the weight distribution of the weighted graph in a natural way. Since an edge in graph data oftentimes represents relationship between two nodes, edge-differential privacy (edge-DP) can protect relationship between two entities from being disclosed. In this paper, we investigate the problem of publishing the node strength histogram of a private graph under edge-DP. We propose two clustering approaches based on sequence-aware and local density to aggregate histogram. Our experimental study demonstrates that our approaches can greatly reduce the error of approximating the true node strength histogram.

[1]  Jon M. Kleinberg,et al.  Wherefore art thou R3579X? , 2011, Commun. ACM.

[2]  Jianliang Xu,et al.  Towards Accurate Histogram Publication under Differential Privacy , 2014, SDM.

[3]  Sofya Raskhodnikova,et al.  Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions , 2015, ArXiv.

[4]  Hongzhi Yin,et al.  Spatio-Temporal Recommendation in Social Media , 2016, SpringerBriefs in Computer Science.

[5]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[6]  Piraveenan Mahendra,et al.  Influence of vaccination strategies and topology on the herd immunity of complex networks , 2014, Social Network Analysis and Mining.

[7]  Yin Yang,et al.  Differentially private histogram publication , 2012, The VLDB Journal.

[8]  Frank McSherry,et al.  Privacy integrated queries: an extensible platform for privacy-preserving data analysis , 2009, SIGMOD Conference.

[9]  Hao Wang,et al.  Adapting to User Interest Drift for POI Recommendation , 2016, IEEE Transactions on Knowledge and Data Engineering.

[10]  Sofya Raskhodnikova,et al.  Smooth sensitivity and sampling in private data analysis , 2007, STOC '07.

[11]  Ninghui Li,et al.  Publishing Graph Degree Distribution with Node Differential Privacy , 2016, SIGMOD Conference.

[12]  Bing-Rong Lin,et al.  Towards an axiomatization of statistical privacy and utility , 2010, PODS.

[13]  Ben Y. Zhao,et al.  Sharing graphs using differentially private graph models , 2011, IMC '11.

[14]  Philip S. Yu,et al.  Differentially Private Data Publishing and Analysis: A Survey , 2017, IEEE Transactions on Knowledge and Data Engineering.

[15]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[16]  Daniel A. Spielman,et al.  Spectral Graph Theory and its Applications , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[17]  Claude Castelluccia,et al.  Differentially Private Histogram Publishing through Lossy Compression , 2012, 2012 IEEE 12th International Conference on Data Mining.

[18]  Yue Wang,et al.  A Data- and Workload-Aware Query Answering Algorithm for Range Queries Under Differential Privacy , 2014, Proc. VLDB Endow..

[19]  David D. Jensen,et al.  Accurate Estimation of the Degree Distribution of Private Networks , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[20]  Cynthia Dwork,et al.  Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography , 2007, WWW '07.

[21]  Sofya Raskhodnikova,et al.  Analyzing Graphs with Node Differential Privacy , 2013, TCC.

[22]  Divesh Srivastava,et al.  Private Release of Graph Statistics using Ladder Functions , 2015, SIGMOD Conference.

[23]  Kunal Talwar,et al.  Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[24]  Sharon Goldberg,et al.  Calibrating Data to Sensitivity in Private Data Analysis , 2012, Proc. VLDB Endow..

[25]  Zi Huang,et al.  Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation , 2016, ACM Trans. Inf. Syst..

[26]  Yang Wang,et al.  SPTF: A Scalable Probabilistic Tensor Factorization Model for Semantic-Aware Behavior Prediction , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[27]  Vitaly Shmatikov,et al.  De-anonymizing Social Networks , 2009, 2009 30th IEEE Symposium on Security and Privacy.

[28]  Ninghui Li,et al.  Understanding Hierarchical Methods for Differentially Private Histograms , 2013, Proc. VLDB Endow..

[29]  Yunhong Wang,et al.  Robust mobile spamming detection via graph patterns , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).