k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 16th international conference on World Wide Web
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
Observing the evolution of internet as topology
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Comparison of online social relations in volume vs interaction: a case study of cyworld
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph annotations in modeling complex network topologies
ACM Transactions on Modeling and Computer Simulation (TOMACS)
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Mining communities in networks: a solution for consistency and its evaluation
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
StarClique: guaranteeing user privacy in social networks against intersection attacks
Proceedings of the 5th international conference on Emerging networking experiments and technologies
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A Differentially Private Graph Estimator
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Class-based graph anonymization for social network data
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Measurement-calibrated graph models for social network experiments
Proceedings of the 19th international conference on World wide web
Deterministic and efficiently searchable encryption
CRYPTO'07 Proceedings of the 27th annual international cryptology conference on Advances in cryptology
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Differentially-private network trace analysis
Proceedings of the ACM SIGCOMM 2010 conference
Differentially private combinatorial optimization
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
A privacy protection technique for publishing data mining models and research data
ACM Transactions on Management Information Systems (TMIS)
Understanding latent interactions in online social networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Differential Privacy via Wavelet Transforms
IEEE Transactions on Knowledge and Data Engineering
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
A differentially private estimator for the stochastic Kronecker graph model
Proceedings of the 2012 Joint EDBT/ICDT Workshops
A workflow for differentially-private graph synthesis
Proceedings of the 2012 ACM workshop on Workshop on online social networks
Evolution of social-attribute networks: measurements, modeling, and implications using google+
Proceedings of the 2012 ACM conference on Internet measurement conference
A Guide to Differential Privacy Theory in Social Network Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Mining frequent graph patterns with differential privacy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 11th Annual Workshop on Network and Systems Support for Games
UMicS: from anonymized data to usable microdata
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A refined complexity analysis of degree anonymization in graphs
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
Hi-index | 0.00 |
Continuing success of research on social and computer networks requires open access to realistic measurement datasets. While these datasets can be shared, generally in the form of social or Internet graphs, doing so often risks exposing sensitive user data to the public. Unfortunately, current techniques to improve privacy on graphs only target specific attacks, and have been proven to be vulnerable against powerful de-anonymization attacks. Our work seeks a solution to share meaningful graph datasets while preserving privacy. We observe a clear tension between strength of privacy protection and maintaining structural similarity to the original graph. To navigate the tradeoff, we develop a differentially-private graph model we call Pygmalion. Given a graph G and a desired level of e-differential privacy guarantee, Pygmalion extracts a graph's detailed structure into degree correlation statistics, introduces noise into the resulting dataset, and generates a synthetic graph G'. G' maintains as much structural similarity to G as possible, while introducing enough differences to provide the desired privacy guarantee. We show that simply applying differential privacy to graphs results in the addition of significant noise that may disrupt graph structure, making it unsuitable for experimental study. Instead, we introduce a partitioning approach that provides identical privacy guarantees using much less noise. Applied to real graphs, this technique requires an order of magnitude less noise for the same privacy guarantees. Finally, we apply our graph model to Internet, web, and Facebook social graphs, and show that it produces synthetic graphs that closely match the originals in both graph structure metrics and behavior in application-level tests.