article

P-syncBB: a privacy preserving branch and bound DCOP algorithm

Authors:

Tal Grinshpoun,

Tamir TassaAuthors Info & Claims

Journal of Artificial Intelligence Research, Volume 57, Issue 1

Pages 621 - 660

Published: 01 September 2016 Publication History

Abstract

Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving branch and bound algorithm for this purpose. The proposed algorithm, P-SyncBB, preserves constraint, topology and decision privacy. The algorithm requires secure solutions to several multi-party computation problems. Consequently, appropriate novel secure protocols are devised and analyzed. An extensive experimental evaluation on different benchmarks, problem sizes, and constraint densities shows that P-SyncBB exhibits superior performance to other privacy-preserving complete DCOP algorithms.

References

[1]

Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286 (5439), 509-512.

[2]

Benaloh, J. (1986). Secret sharing homomorphisms: Keeping shares of a secret secret. In Crypto, pp. 251-260.

[3]

Blake, I. F., & Kolesnikov, V. (2004). Strong conditional oblivious transfer and computing on intervals. In ASIACRYPT, pp. 515-529.

[4]

Brito, I., Meisels, A., Meseguer, P., & Zivan, R. (2009). Distributed constraint satisfaction with partially known constraints. Constraints, 14 (2), 199-234.

Digital Library

[5]

Chechetka, A., & Sycara, K. (2006). No-commitment branch and bound search for distributed constraint optimization. In AAMAS, pp. 1427-1429.

[6]

Dechter, R. (2003). Constraint Processing. Morgan Kaufman.

[7]

Doshi, P., Matsui, T., Silaghi, M. C., Yokoo, M., & Zanker, M. (2008). Distributed private constraint optimization. In WI-IAT, pp. 277-281.

[8]

Even, S., Goldreich, O., & Lempel, A. (1985). A randomized protocol for signing contracts. Communications of the ACM, 28, 637-647.

Digital Library

[9]

Faltings, B., Léauté, T., & Petcu, A. (2008). Privacy guarantees through distributed constraint satisfaction. In WI-IAT, pp. 350-358.

[10]

Faltings, B., & Macho-Gonzalez, S. (2005). Open constraint programming. Artificial Intelligence, 161:1-2, 181-208.

Digital Library

[11]

Fischlin, M. (2001). A cost-effective pay-per-multiplication comparison method for millionaires. In Topics in Cryptology - CT-RSA, pp. 457-472.

[12]

Freuder, E. C., & Quinn, M. J. (1985). Taking advantage of stable sets of variables in constraint satisfaction problems. In IJCAI, pp. 1076-1078.

[13]

Gershman, A., Zivan, R., Grinshpoun, T., Grubshtein, A., & Meisels, A. (2008). Measuring distributed constraint optimization algorithms. In DCR Workshops, pp. 17-24.

[14]

Gershman, A., Meisels, A., & Zivan, R. (2009). Asynchronous forward bounding for distributed COPs. Journal of Artificial Intelligence Research, 34, 61-88.

[15]

Greenstadt, R., Pearce, J., & Tambe, M. (2006). Analysis of privacy loss in distributed constraint optimization. In AAAI, pp. 647-653.

[16]

Greenstadt, R., Grosz, B., & Smith, M. D. (2007). SSDPOP: improving the privacy of DCOP with secret sharing. In AAMAS, pp. 171:1-171:3.

[17]

Grinshpoun, T., & Meisels, A. (2008). Completeness and performance of the APO algorithm. Journal of Artificial Intelligence Research, 33, 223-258.

Digital Library

[18]

Grinshpoun, T. (2012). When you say (DCOP) privacy, what do you mean? - categorization of DCOP privacy and insights on internal constraint privacy. In ICAART, pp. 380- 386.

[19]

Grinshpoun, T., Grubshtein, A., Zivan, R., Netzer, A., & Meisels, A. (2013). Asymmetric distributed constraint optimization problems. Journal of Artificial Intelligence Research, 47, 613-647.

[20]

Grinshpoun, T., & Tassa, T. (2014). A privacy-preserving algorithm for distributed constraint optimization. In AAMAS, pp. 909-916.

[21]

Gutierrez, P., Meseguer, P., & Yeoh, W. (2011). Generalizing ADOPT and BnB-ADOPT. In IJCAI, pp. 554-559.

[22]

Hirayama, K., & Yokoo, M. (1997). Distributed partial constraint satisfaction problem. In CP, pp. 222-236.

[23]

Ioannidis, I., & Grama, A. (2003). An Efficient protocol for yao's millionaires' problem. In HICSS, p. 205.

[24]

Jackson, M. O. (2008). Social and Economic Networks. Princeton University Press.

[25]

Jeckmans, A., Tang, Q., & Hartel, P. H. (2012). Privacy-preserving collaborative filtering based on horizontally partitioned dataset. In CTS, pp. 439-446.

[26]

Jiang, W., & Clifton, C. (2006). A secure distributed framework for achieving k-anonymity. The VLDB Journal, 15, 316-333.

Digital Library

[27]

Kantarcioglu, M., & Clifton., C. (2004). Privacy-preserving distributed mining of association rules on horizontally partitioned data. Transactions on Knowledge and Data Engineering, 16, 1026-1037.

Digital Library

[28]

Léauté, T., & Faltings, B. (2011). Distributed constraint optimization under stochastic uncertainty. In AAAI, pp. 68-73.

[29]

Léauté, T., & Faltings, B. (2013). Protecting privacy through distributed computation in multi-agent decision making. Journal of Artificial Intelligence Research, 47, 649-695.

[30]

Lindell, Y., & Pinkas, B. (2000). Privacy preserving data mining. In Crypto, pp. 36-54.

[31]

Lutati, B., Gontmakher, I., Lando, M., Netzer, A., Meisels, A., & Grubshtein, A. (2014). Agentzero: A framework for simulating and evaluating multi-agent algorithms. In Agent-Oriented Software Engineering, pp. 309-327. Springer.

[32]

Maheswaran, R. T., Tambe, M., Bowring, E., Pearce, J. P., & Varakantham, P. (2004). Taking DCOP to the real world: Efficient complete solutions for distributed multievent scheduling. In AAMAS, pp. 310-317.

[33]

Maheswaran, R., Pearce, J., Bowring, E., Varakantham, P., & Tambe, M. (2006). Privacy loss in distributed constraint reasoning: A quantitative framework for analysis and its applications. Autonomous Agents and Multi-Agent Systems, 13, 27-60.

Digital Library

[34]

Mailler, R., & Lesser, V. R. (2004). Solving distributed constraint optimization problems using cooperative mediation. In AAMAS, pp. 438-445.

[35]

Modi, J., & Veloso, M. (2004). Multiagent meeting scheduling with rescheduling. In DCR Workshops.

[36]

Modi, P. J., Shen, W., Tambe, M., & Yokoo, M. (2005). ADOPT: asynchronous distributed constraints optimization with quality guarantees. Artificial Intelligence, 161, 149-180.

Digital Library

[37]

Nissim, K., & Zivan, R. (2005). Secure DisCSP protocols - from centralized towards distributed solutions. In DCR Workshops.

[38]

Paillier, P. (1999). Public-key cryptosystems based on composite degree residuosity classes. In Eurocrypt, pp. 223-238.

Digital Library

[39]

Petcu, A., & Faltings, B. (2005). A scalable method for multiagent constraint optimization. In IJCAI, pp. 266-271.

[40]

Rabin, M. O. (1981). How to exchange secrets by oblivious transfer. Tech. rep. TR-81, Aiken Computation Laboratory, Harvard University.

[41]

Rivest, R. L., Shamir, A., & Adleman, L. M. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21, 120-126.

Digital Library

[42]

Schuster, A., Wolff, R., & Gilburd, B. (2004). Privacy-preserving association rule mining in large-scale distributed systems. In CCGrid, pp. 411-418.

[43]

Silaghi, M. C., Faltings, B., & Petcu, A. (2006). Secure combinatorial optimization simulating DFS tree-based variable elimination. In ISAIM.

[44]

Silaghi, M. C., & Mitra, D. (2004). Distributed constraint satisfaction and optimization with privacy enforcement. In IAT, pp. 531-535.

[45]

Sultanik, E., Lass, R. N., & Regli, W. C. (2008). DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms. In AAMAS (demos), pp. 1667-1668.

Digital Library

[46]

Tassa, T. (2014). Secure mining of association rules in horizontally distributed databases. Transactions on Knowledge and Data Engineering, 26, 970-983.

Digital Library

[47]

Tassa, T., & Gudes, E. (2012). Secure distributed computation of anonymized views of shared databases. Transactions on Database Systems, 37, Article 11.

Digital Library

[48]

Tassa, T., & Bonchi, F. (2014). Privacy preserving estimation of social influence. In EDBT, pp. 559-570.

[49]

Tassa, T., & Cohen, D. J. (2013). Anonymization of centralized and distributed social networks by sequential clustering. Transactions on Knowledge and Data Engineering, 25, 311-324.

Digital Library

[50]

Tassa, T., Zivan, R., & Grinshpoun, T. (2015). Max-sum goes private. In IJCAI, pp. 425-431.

[51]

Tassa, T., Zivan, R., & Grinshpoun, T. (2016). Preserving privacy in region optimal DCOP algorithms. In IJCAI, pp. 496-502.

[52]

Vaidya, J., & Clifton, C. (2002). Privacy preserving association rule mining in vertically partitioned data. In KDD, pp. 639-644.

[53]

Yakut, I., & Polat, H. (2012). Arbitrarily distributed data-based recommendations with privacy. Data & Knowledge Engineering, 72, 239-256.

Digital Library

[54]

Yao, A. (1982). Protocols for secure computation. In FOCS, pp. 160-164.

[55]

Yeoh, W., Felner, A., & Koenig, S. (2010). BnB-ADOPT: An asynchronous branch-and-bound DCOP algorithm. Journal of Artificial Intelligence Research, 38, 85-133.

[56]

Yokoo, M., Suzuki, K., & Hirayama, K. (2005). Secure distributed constraints satisfaction: Reaching agreement without revealing private information. Artificial Intelligence, 161, 229-246.

Digital Library

[57]

Yokoo, M., & Hirayama, K. (2000). Algorithms for distributed constraint satisfaction: A review. Autonomous Agents and Multi-Agent Systems, 3 (2), 185-207.

[58]

Zhan, J., Matwin, S., & Chang, L. (2005). Privacy preserving collaborative association rule mining. In Data and Applications Security, pp. 153-165.

Digital Library

[59]

Zhang, W., Xing, Z., Wang, G., & Wittenburg, L. (2005). Distributed stochastic search and distributed breakout: Properties, comparishon and applications to constraints optimization problems in sensor networks. Artificial Intelligence, 161, 55-88.

Digital Library

[60]

Zhong, S., Yang, Z., & Wright, R. (2005). Privacy-enhancing k-anonymization of customer data. In PODS, pp. 139-147.

[61]

Zivan, R., & Meisels, A. (2006). Message delay and DisCSP search algorithms. Annals of Mathematics and Artificial Intelligence, 46, 415-439.

Digital Library

[62]

Zivan, R., Okamoto, S., & Peled, H. (2014). Explorative anytime local search for distributed constraint optimization. Artificial Intelligence, 212, 1-26.

Digital Library

Cited By

Tassa THorin A(2022)Privacy-preserving Collaborative Filtering by Distributed MediationACM Transactions on Intelligent Systems and Technology10.1145/354295013:6(1-26)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3542950
Matsui T(2021)Negotiation Considering Privacy Loss on Asymmetric Multi-objective Decentralized Constraint Optimization ProblemAgents and Artificial Intelligence10.1007/978-3-031-10161-8_5(85-110)Online publication date: 4-Feb-2021
https://dl.acm.org/doi/10.1007/978-3-031-10161-8_5
Hoang KYeoh WYokoo MRabinovich ZEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)New Algorithms for Continuous Distributed Constraint Optimization ProblemsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398823(502-510)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398823
Show More Cited By

Recommendations

Achieving P-Sensitive K-Anonymity via Anatomy
ICEBE '09: Proceedings of the 2009 IEEE International Conference on e-Business Engineering

Privacy-preserving data publishing is to protect sensitive information of individuals in published data while the distortion ratio of the data is minimized. One well-studied approach is the $k$-anonymity model. Recently, several authors have recognized ...
P-Sensitive K-Anonymity with Generalization Constraints

Numerous privacy models based on the k-anonymity property and extending the k-anonymity model have been introduced in the last few years in data privacy research: l-diversity, p-sensitive k-anonymity, (α, k) anonymity, t-closeness, etc. While differing ...
Personalized sensitive attribute anonymity based on P - sensitive k anonymity
ICIIP '16: Proceedings of the 1st International Conference on Intelligent Information Processing

With the development of science and technology, privacy protection has also been highly valued. Existing anonymity algorithms are only anonymous quasi-identifier to achieve privacy protection, but ignore the sensitive properties of the personalized ...

Comments

Information & Contributors

Information

Published In

cover image Journal of Artificial Intelligence Research

Journal of Artificial Intelligence Research Volume 57, Issue 1

September 2016

673 pages

ISSN:1076-9757

Issue’s Table of Contents

Publisher

AI Access Foundation

El Segundo, CA, United States

Publication History

Published: 01 September 2016

Published in JAIR Volume 57, Issue 1

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tassa THorin A(2022)Privacy-preserving Collaborative Filtering by Distributed MediationACM Transactions on Intelligent Systems and Technology10.1145/354295013:6(1-26)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3542950
Matsui T(2021)Negotiation Considering Privacy Loss on Asymmetric Multi-objective Decentralized Constraint Optimization ProblemAgents and Artificial Intelligence10.1007/978-3-031-10161-8_5(85-110)Online publication date: 4-Feb-2021
https://dl.acm.org/doi/10.1007/978-3-031-10161-8_5
Hoang KYeoh WYokoo MRabinovich ZEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)New Algorithms for Continuous Distributed Constraint Optimization ProblemsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398823(502-510)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398823
Shmueli ETassa T(2020)Mediated Secure Multi-Party Protocols for Collaborative FilteringACM Transactions on Intelligent Systems and Technology10.1145/337540211:2(1-25)Online publication date: 24-Feb-2020
https://dl.acm.org/doi/10.1145/3375402
Chen DDeng YChen ZHe ZZhang W(2020)A hybrid tree-based algorithm to solve asymmetric distributed constraint optimization problemsAutonomous Agents and Multi-Agent Systems10.1007/s10458-020-09476-534:2Online publication date: 21-Jul-2020
https://dl.acm.org/doi/10.1007/s10458-020-09476-5
Tassa TGrinshpoun TYanai A(2019)A privacy preserving collusion secure DCOP algorithmProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367707(4774-4780)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367707
Matsui T(2019)A Study of Cooperation with Privacy Loss Based on Asymmetric Constraint Optimization Problem Among AgentsProceedings of the 3rd International Conference on Advances in Artificial Intelligence10.1145/3369114.3369145(127-134)Online publication date: 26-Oct-2019
https://dl.acm.org/doi/10.1145/3369114.3369145
Fioretto FYeoh W(2018)AI buzzwords explainedAI Matters10.1145/3175502.31755063:4(8-13)Online publication date: 16-Feb-2018
https://dl.acm.org/doi/10.1145/3175502.3175506
Kong SLee JLi S(2018)A new distributed algorithm for efficient generalized arc-consistency propagationAutonomous Agents and Multi-Agent Systems10.1007/s10458-018-9388-x32:5(569-601)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10458-018-9388-x
Tassa TGrinshpoun TZivan R(2017)Privacy preserving implementation of the max-sum algorithm and its variantsJournal of Artificial Intelligence Research10.5555/3176788.317679659:1(311-349)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.5555/3176788.3176796
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents