Heterogeneous Identity Expression and Association Method Based on Attribute Aggregation
Keywords:Heterogeneous Identity Alliance, Attribute aggregation, Network Identity Management, Identity expression, Trust management
Existing identity expression methods are often limited in a single security domain, and this is inadequate to meet the cross-domain access requirements of heterogeneous networks. In view of this problem, we propose an index system for the ubiquitous expression of heterogeneous identities, and introduce the concept pair matching based attribute aggregation method by combining the characteristics of heterogeneous identity alliances. The selection of concept pairs considers the original meaning of attribute characteristics, including the lexical level, i.e., class, ontology, label, description, the structural level, i.e., position, distance between nodes, and the semantic level, i.e., formal concept analysis. As for the attribute aggregation, if multiple attributes from a heterogeneous network contain the same or similar concepts, they are considered the same attribute for the user identity in a heterogeneous network. Relevant domain knowledge or heuristic knowledge will adjust the result of attribute aggregation, and the constraint relationship between conceptual structures are used to adjust and optimize the attribute aggregation set. Based on the identity attribute index system of the heterogeneous identity alliance, the identity similarity evaluation results based on each attribute are generated. When the comprehensively considered identity similarity evaluation result is higher than the empirical threshold, the heterogeneous identity alliance has different trusts for the same user. The experimental results show that our scheme has a better overall aggregation effect on identity attribute aggregation.
E. Sanzi, S. A. Demurjia and J. Billings, ‘Integrating Trust Profiles, Trust Negotiation, and Attribute Based Access Control,’ in 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), San Francisco, CA, USA, 2017, pp. 177-184.
I. Lee and K. Lee, ‘The Internet of Things (IoT): Applications, investments, and challenges for enterprises,’, Business Horizons, vol. 58, no. 4, pp. 431-440, 2015.
D. C. Hardt, ‘Auditable privacy policies in a distributed hierarchical identity management system,’ U.S. Patent 9,245,266, Jan. 26, 2016.
G. J. Ahn, ‘Identity selector for use with a user-portable device and method of use in a user-centric identity management system,’ U.S. Patent 9,935,935, Apr. 3, 2018.
S. M. Smith and D. Khovratovich, ‘Identity System Essentials,’. Evemyrn, 2016, pp. 16.
K. Fragkiadaki, S. Levine, P. Felsen, et al. ‘Recurrent network models for human dynamics,’ in Proceedings of the IEEE International Conference on Computer Vision, Washington, DC, USA, 2015, pp. 4346-4354.
M. Kohtamäki, S. Thorgren and J. Wincent, ‘Organizational identity and behaviors in strategic networks,’ Journal of Business & Industrial Marketing, vol. 31, no. 1, pp. 36-46, 2016.
M. Giroux, ‘From Identity to Alliance: Challenging Métis Inauthenticity through Alliance Studies,’ Yearbook for Traditional Music, vol 50, pp. 91-118, 2018.
J. Werner, C. M. Westphall, C. B. Westphall, ‘Cloud identity management: A survey on privacy strategies,’ Computer Networks, vol 122, pp. 29-42, 2017.
F. Gonçalves, B. Ribeiro, O. Gama, et al. ‘Hybrid model for secure communications and identity management in vehicular ad hoc networks,’ in 2017 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Munich, Germany, 2017, pp. 414-422.
P. Modesti, T. Gross, S. Mödersheim, et al. ‘Security Evaluation of FutureID,’ FurtureID, 2015. [Online]. Available: http://www.futureid.eu/data/deliverables/year3/Public/FutureID_D12.03_WP12_v1.0_Security_Evaluation.pdf.
A. K. Pathan, ‘Security of self-organizing networks: MANET, WSN, WMN, VANET,’ CRC press, 2016.
J. W. Rittinghous and J. F. Ransome, ‘Cloud computing: implementation, management, and security,’ CRC press, 2017.
P. Gao, J. S. Baras and J. Golbeck, ‘Semiring-based trust evaluation for information fusion in social network services,’ in 18th international conference on information fusion (Fusion), Washington, DC, USA, July, 2015, pp. 590-596.
Y. Du, X. Du and L. Huang, ‘Improve the collaborative filtering recommender system performance by trust network construction,’ Chinese Journal of Electronics, vol 25, no. 3, pp. 418-423, 2016.
H. Han, A. K. Jain, F. Wang, et al. ‘Heterogeneous face attribute estimation: A deep multi-task learning approach,’ IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 11, pp. 2597-2609, 2017.
B. Eze, C. Kuziemsky C and L. Peyton L, ‘A patient identity matching service for cloud-based performance management of community healthcare,’ Procedia computer science, vol. 112, pp. 287-294, 2017.
R. Oppliger, ‘Microsoft .net passport: A security analysis,’ Computer, vol. 36, no. 7, pp. 29-35, 2003.
B. Ellin, “About openlD,” [Online]. Available: http://www.openidenabled.com/openid/about-openid, 2006.
L. Alliance, ‘Liberty alliance project，’[Online]. Available: http://www.projectliberty.org 24 (2002).
K. Günter, ‘Access control with IBM Tivoli access manager,’ Acm Transactions on Information & System Security, vol. 6, no. 2, pp. 232-257, 2003.
Novell, ‘Identity cloud,’[Online]. Available: http://novell.com/ichain.
D. Hardt, ‘The OAuth 2.0 authorization framework,’[Online]. Available: http://tools.ietf.org/html/rfc6749.
T. Martens, ‘Electronic identity management in Estonia between market and state governance,’ Identity in the Information Society, vol. 3, no. 1, pp. 213-233, 2010.
H. Kubicek, ‘Introduction: conceptual framework and research design for a comparative analysis of national eID Management Systems in selected European countries,’ Identity in the Information Society, vol. 3, no. 1, pp. 5-26, 2010.
T. Lenz and B. Zwattendorfer, ‘A Modular and Flexible Identity Management Architecture for National eID Solutions,’ International Conference on Web Information Systems and Technologies. 2015, pp. 321-331.
A. Barbir, ‘Identity attribute exchange and validation broker,’ U.S. Patent 8,935,808, Jan. 13, 2015.
J. Wang J, X. Zhu, S. Gong, et al. ‘Transferable joint attribute-identity deep learning for unsupervised person re-identification,’ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, 2018, pp. 2275-2284.
G. Bella, F. Giunchiglia, F. McNeill, ‘Language and domain aware lightweight ontology matching,’ Journal of Web Semantics, vol. 43, 43, pp. 1-17, 2017.
L. Asprino, V. Presutti, A. Gangemi, et al, ‘Frame-based ontology alignment,’ Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 2017.
M. Fahad, ‘Merging of axiomatic definitions of concepts in the complex OWL ontologies,’ Artificial Intelligence Review, vol. 47, no. 2, pp. 181-215, 2017.
Z. Yan, L. Zhang, W. Ding W, et al, ‘Heterogeneous data storage management with deduplication in cloud computing,’ IEEE Transactions on Big Data, vol. 5, no. 3, pp. 393 - 407,2017.
J. Leskovec, J. Mcauley, ‘Learning to discover social circles in ego networks,’Advances in neural information processing systems, 2012, Tillamook, USA, pp. 539-547.