Group Differential Privacy-Preserving Disclosure of Multi-level Association Graphs
Title | Group Differential Privacy-Preserving Disclosure of Multi-level Association Graphs |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Palanisamy, B., Li, C., Krishnamurthy, P. |
Conference Name | 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) |
ISBN Number | 978-1-5386-1792-2 |
Keywords | aggregate information, Aggregates, Bipartite graph, Computing Theory, Data analysis, data privacy, data protection, drugs, Electronic mail, graph data, graph theory, group differential privacy-preserving disclosure, group privacy, Human Behavior, human factor, information privacy protection, multilevel association graphs, privacy, pubcrawl, resilience, Resiliency, Scalability, εg-group differential privacy |
Abstract | Traditional privacy-preserving data disclosure solutions have focused on protecting the privacy of individual's information with the assumption that all aggregate (statistical) information about individuals is safe for disclosure. Such schemes fail to support group privacy where aggregate information about a group of individuals may also be sensitive and users of the published data may have different levels of access privileges entitled to them. We propose the notion ofeg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined privacy levels, enabling data users to obtain the level of access entitled to them. We present a preliminary evaluation of the proposed notion of group privacy through experiments on real association graph data that demonstrate the guarantees on group privacy on the disclosed data. |
URL | https://ieeexplore.ieee.org/document/7980244/ |
DOI | 10.1109/ICDCS.2017.223 |
Citation Key | palanisamy_group_2017 |
- group differential privacy-preserving disclosure
- εg-group differential privacy
- Scalability
- Resiliency
- resilience
- pubcrawl
- privacy
- multilevel association graphs
- information privacy protection
- human factor
- Human behavior
- group privacy
- aggregate information
- graph theory
- graph data
- Electronic mail
- drugs
- Data protection
- data privacy
- data analysis
- Computing Theory
- Bipartite graph
- Aggregates