Metadata-Conscious Anonymous Messaging

Anonymous messaging platforms allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. Popular demand for anonymous messaging is evidenced by the success of mobile apps like Whisper and Yik Yak. In such platforms, the spread of messages is typically modeled as a diffusion process. Recent advances in network analysis have revealed that such diffusion processes are vulnerable to author deanonymization by adversaries with access to metadata, such as timing information. In this work, we ask the fundamental question of how to intervene in the propagation of anonymous messages in order to make it difficult to find the source. In particular, we study the performance of a message propagation protocol called adaptive diffusion. We prove that it achieves asymptotically optimal source-hiding and significantly outperforms standard diffusion. We further demonstrate empirically that adaptive diffusion hides the source effectively on real social graphs.

[1]  Luc Devroye,et al.  Finding Adam in random growing trees , 2014, Random Struct. Algorithms.

[2]  Shie Mannor,et al.  Detecting epidemics using highly noisy data , 2013, MobiHoc.

[3]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[4]  Bryan Ford,et al.  Dissent: accountable anonymous group messaging , 2010, CCS '10.

[5]  Martin Vetterli,et al.  Locating the Source of Diffusion in Large-Scale Networks , 2012, Physical review letters.

[6]  David Chaum,et al.  The dining cryptographers problem: Unconditional sender and recipient untraceability , 1988, Journal of Cryptology.

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

[8]  Patrick Valduriez,et al.  Protecting Data Privacy in Structured P2P Networks , 2009, Globe.

[9]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[10]  Stefan Lindskog,et al.  How the Great Firewall of China is Blocking Tor , 2012, FOCI.

[11]  Varun Jog,et al.  Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process , 2016, ArXiv.

[12]  Devavrat Shah,et al.  Rumors in a Network: Who's the Culprit? , 2009, IEEE Transactions on Information Theory.

[13]  Hongyuan Zha,et al.  Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades , 2015, AISTATS.

[14]  Dan Boneh,et al.  Riposte: An Anonymous Messaging System Handling Millions of Users , 2015, 2015 IEEE Symposium on Security and Privacy.

[15]  Kannan Ramchandran,et al.  Rumor Source Obfuscation on Irregular Trees , 2016, SIGMETRICS.

[16]  Lei Ying,et al.  Locating Contagion Sources in Networks with Partial Timestamps , 2014, ArXiv.

[17]  Pramod Viswanath,et al.  Spy vs. Spy , 2014, SIGMETRICS.

[18]  John O. Koehler.  Stasi: The Untold Story Of The East German Secret Police , 1999 .

[19]  Refik Molva,et al.  Safebook: A privacy-preserving online social network leveraging on real-life trust , 2009, IEEE Communications Magazine.

[20]  Wuqiong Luo,et al.  How to Identify an Infection Source With Limited Observations , 2013, IEEE Journal of Selected Topics in Signal Processing.

[21]  George Danezis,et al.  DP5: A Private Presence Service , 2015, Proc. Priv. Enhancing Technol..

[22]  Shie Mannor,et al.  On identifying the causative network of an epidemic , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[23]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[24]  Shie Mannor,et al.  Network forensics: random infection vs spreading epidemic , 2012, SIGMETRICS '12.

[25]  Norman L. Johnson,et al.  Urn models and their application , 1977 .

[26]  Varun Jog,et al.  Persistence of centrality in random growing trees , 2015, Random Struct. Algorithms.

[27]  Benjamin Greschbach,et al.  The devil is in the metadata — New privacy challenges in Decentralised Online Social Networks , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops.

[28]  Vincenzo Fioriti,et al.  The topological defense in SIS epidemic models , 2016 .

[29]  Vincenzo Fioriti,et al.  Predicting the sources of an outbreak with a spectral technique , 2012, ArXiv.

[30]  Lei Ying,et al.  Locating the contagion source in networks with partial timestamps , 2015, Data Mining and Knowledge Discovery.

[31]  Christos Faloutsos,et al.  Spotting Culprits in Epidemics: How Many and Which Ones? , 2012, 2012 IEEE 12th International Conference on Data Mining.

[32]  Chee Wei Tan,et al.  Rumor source detection with multiple observations: fundamental limits and algorithms , 2014, SIGMETRICS '14.

[33]  Po-Ling Loh,et al.  Confidence Sets for the Source of a Diffusion in Regular Trees , 2015, IEEE Transactions on Network Science and Engineering.