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Privacy preserving existence recognition and construction of hypertree agent organization

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Abstract

Decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning are some essential tasks of cooperative multiagent systems. Several frameworks for these tasks organize agents into a junction tree (JT). We show that existing techniques for JT existence recognition and construction leak information on private variables, shared variables, agent identities and adjacency, that can potentially be protected. We present a scheme to quantify these privacy losses. We develop two novel algorithms for JT existence recognition and for JT construction when existing, that provide strong guarantee of agent privacy. Our experimental comparison shows that the proposed algorithms out-perform existing techniques, one of them having the lowest privacy loss and the other having no privacy loss, while being more efficient than most alternatives.

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Notes

  1. WebWeavr is a Java-based toolkit for graphical models and is available at http://www.socs.uoguelph.ca/~yxiang/.

References

  1. Awerbuch, B. (1987). Optimal distributed algorithms for minimum weight spanning tree, counting, leader election, and related problems. In Proceedings of 19th symposium on the theory of computing (pp. 230–240).

  2. Brito, I., & Meseguer, P. (2010). Cluster tree elimination for distributed constraint optimization with quality guarantees. Fundamenta Informaticae, 102(3–4), 263–286.

    MathSciNet  MATH  Google Scholar 

  3. Dechter, R. (2003). Constraint processing. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  4. Doshi, P., Matsui, T., Silaghi, M., Yokoo, M., & Zanker, M. (2008). Distributed private constraint optimization. In Proceedings of international conference on web intelligence and intelligent agent technology (pp. 277–281).

  5. Faloutsos, M., & Molle, M. (1995). Optimal distributed algorithm for minimum spanning trees revisited. In Proceedings of the 14th annual ACM symposium on principles of distributed computing (pp. 231–237).

  6. Faltings, B., Leaute, T., & Petcu, A. (2008) Privacy guarantees through distributed constraint satisfaction. In Proceedings of the IEEE/WIC/ACM intelligent agent technology (pp. 350–358).

  7. Gallager, R. G., Humblet, P. A., & Spira, P. M. (1983). A distributed algorithm for minimum-weight spanning trees. ACM Transactions on Programming Languages and Systems, 5(1), 66–77.

    Article  MATH  Google Scholar 

  8. Garay, J., Kutten, S., & Peleg, D. (1998). A sublinear time distributed algorithm for minimum-weight spanning trees. SIAM Journal on Computing, 27(1), 302–316.

    Article  MathSciNet  MATH  Google Scholar 

  9. Gmytrasiewicz, P. J., & Durfee, E. H. (2001). Rational communication in multi-agent environments. Autonomous Agents and Multi-Agent Systems, 4(3), 233–272.

    Article  Google Scholar 

  10. Greenstadt, R., Pearce, J.P., Bowring, E. & Tambe, M. (2006). Experimental analysis of privacy loss in DCOP algorithms. In Proceedings of international joint conference on autonomous agents and multiagent systems (pp. 1424–1426).

  11. Jensen, F. V. (1988). Junction tree and decomposable hypergraphs. Aalborg, Denmark: JUDEX. Technical report.

  12. Khan, M., & Pandurangan, G. (2008). A fast distributed approximation algorithm for minimum spanning trees. Distributed Computing, 20(6), 391–402.

    Article  MATH  Google Scholar 

  13. Koller, D., & Milch, B. (2001). Multi-agent influence diagrams for representing and solving games. In Proceedings of 17th international joint conference on artificial intelligence (pp. 1027–1034).

  14. Kutten, S., & Peleg, D. (1998). Fast distributed construction of smallk-dominating sets and applications. Journal of Algorithms, 28(1), 40–66.

    Article  MathSciNet  MATH  Google Scholar 

  15. Leaute, T., Ottens, B., & Faltings, B. (2010). Ensuring privacy through distributed computation in multiple-depot vehicle routing problems. In Proceedings of the ECAI workshop on artificial intelligence and logistics (pp. 25–30).

  16. Maestre, A., & Bessiere, C. (2004). Improving asynchronous backtracking for dealing with complex local problems. In Proceedings of the 16th European conference on artificial intelligence (pp. 206–210).

  17. Maheswaran, R. T., Pearce, J. P., Bowring, E., Varakantham, P., & Tambe, M. (2006). Privacy loss in distributed constraint reasoning: A quantitative framework for analysis and its applications. Journal of Autonomous Agents and Multi-Agent Systems, 13(1), 27–60.

    Article  Google Scholar 

  18. Maheswaran, R. T., Tambe, M., Bowring, E., Pearce, J. P., & Varakantham, P. (2015). Taking DCOP to the realworld: Efficient complete solutions for distributed multi-event scheduling. In D. Kudenko, D. Kazakov, & E. Alonso (Eds.), Proceedings of 2004 international conference on autonomous agents and multiagent systems, LNCS (LNAI) 3394 (pp. 310–317). Heidelberg: Springer.

    Google Scholar 

  19. Modi, P. J., Shen, W., Tambe, M., & Yokoo, M. (2005). Adopt: asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligences, 161(1–2), 149–180.

    Article  MathSciNet  MATH  Google Scholar 

  20. Nobari, S., Cao, T. T., Karras, P., & Bressan, S. (2012) Salable parallel minimum spanning forest computation. In Proceedings of the 17th ACM SIGPLAN symposium principles and practice of parallel programming (pp. 205–214).

  21. Paskin, M., Guestrin, C., & McFadden, J. (2005). A robust architecture for distributed inference in sensor networks. In Proceedings of the information processing in sensor networks (pp. 55–62).

  22. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  23. Petcu, A., & Faltings, B. (2005). A scalable method for multiagent constraint optimization. In Proceedings of the 19th international joint conference on artificial intelligence (pp. 266–271).

  24. Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell Systems Technical Journal, 36, 1389–1401.

    Article  Google Scholar 

  25. Silaghi, M. C., Abhyankar, A., Zanker, M., & Bartak, R. (2005). Desk-mates (stable matching) with privacy of preferences, and a new distributed CSP framework. In Proceedings of the international Florida artificial intelligence research society conference (pp. 83–96).

  26. Silaghi, M. C., Doshi, P., Matsui, T., & Yokoo, M. (2008). Distributed private constraint optimization problem: Cost of privacy loss. In Proceedings of the AAMAS workshop on distributed constraint reasoning (pp. 1–12).

  27. Silaghi, M. C., & Faltings, B. (2005). Asynchronous aggregation and consistency in distributed constraint satisfaction. Artificial Intelligence, 161(1–2), 25–54.

    Article  MathSciNet  MATH  Google Scholar 

  28. Silaghi, M.C., Faltings, B., & Petcu, A. (2006). Secure combinatorial optimization simulating DFS tree-based variable elimination. In Proceedings of the 9th international symposium on Artificial Intelligence and Mathematics (pp. 1–10).

  29. Silaghi, M. C., & Rajeshirke, V. (2004). The effect of policies for selecting the solution of a DisCSP on privacy loss. In Proceedings of the 3rd international joint conference on autonomous agents and multiagent systems (pp. 1396–1397).

  30. Silaghi, M. C., Sam-Haroud, D., & Faltings, B. (2000). Asynchronous search with aggregations. In Proceedings of AAAI’2000 (pp. 917–922).

  31. Silaghi, M. C., & Yokoo, M. (2008). ADOPT-ing: Unifying asynchronous distributed optimization with asynchronous backtracking. Journal of Autonomous Agents and Multi-Agent Systems, 19(2), 89–123.

    Article  Google Scholar 

  32. Valtorta, M., Kim, Y. G., & Vomlel, J. (2002). Soft evidential update for probabilistic multiagent systems. International Journal of Approximate Reasoning, 29(1), 71–106.

    Article  MathSciNet  MATH  Google Scholar 

  33. Vinyals, M., Rodriguez-Aguilar, J. A., & Cerquides, J. (2010). Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law. Journal of Autonomous Agents and Multi-Agent Systems, 22(3), 439–464.

    Article  Google Scholar 

  34. Xiang, Y. (1996). A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication. Artificial Intelligence, 87(1–2), 295–342.

    Article  MathSciNet  Google Scholar 

  35. Xiang, Y. (2002). Probabilistic reasoning in multiagent systems: A graphical models approach. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  36. Xiang, Y. (2008). Building intelligent sensor networks with multiagent graphical models. In N. Ichalkaranje, G. P. Wren, & L. C. Jain (Eds.), Intelligent decision making: An AI-based approach (pp. 289–320). Heidelberg: Springer.

    Chapter  Google Scholar 

  37. Xiang, Y., Chen, J., & Deshmukh, A. (2004). A decision-theoretic graphical model for collaborative design on supply chains. In A. Y. Tawfik & S. D. Goodwin (Eds.), Advances in artificial intelligence, LNAI 3060 (pp. 355–369). Heidelberg: Springer.

    Google Scholar 

  38. Xiang, Y., & Hanshar, F. (2011). Multiagent expedition with graphical models. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 19(6), 939–976.

    Article  MathSciNet  MATH  Google Scholar 

  39. Xiang, Y., Mohamed, Y., & Zhang, W. (2014). Distributed constraint satisfaction with multiply sectioned constraint networks. International Journal of Information and Decision Sciences, 6(2), 127–152.

    Article  Google Scholar 

  40. Xiang, Y., Pant, B., Eisen, A., Beddoes, M., & Poole, D. (1993). Multiply sectioned Bayesian networks for neuromuscular diagnosis. Artificial Intelligence in Medicine, 5, 293–314.

    Article  Google Scholar 

  41. Xiang, Y., & Srinivasan, K. (2013). Boundary set based existence recognition and construction of hypertree agent organization. In O. R. Zaiane & S. Zilles (Eds.), Advances in artificial intelligence, LNAI 7884 (pp. 187–198). Berlin: Springer.

    Chapter  Google Scholar 

  42. Xiang, Y., & Srinivasan, K. (2013). Construction of privacy preserving hypertree agent organization as distributed maximum spanning tree. In O. R. Zaiane & S. Zilles (Eds.), Advances in artificial intelligence, LNAI 7884 (pp. 199–210). Berlin: Springer.

    Chapter  Google Scholar 

  43. Xiang, Y., & Zhang, W. (2007). Multiagent constraint satisfaction with multiply sectioned constraint networks. In Z. Kobti & D. Wu (Eds.), Advances in artificial intelligence, LNAI 4509 (pp. 228–240). Berlin: Springer.

    Google Scholar 

  44. Xiang, Y., & Zhang, W. (2008). Distributed university timetabling with multiply sectioned constraint networks. In Proceedings of the 21th international Florida artificial intelligence research society conference (pp. 567–572).

  45. Yokoo, M. (2001). Distributed constraint satisfaction. Berlin: Springer.

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  47. Zanker, M., Jannach, D., Silaghi, M. C. & Friedrich, G. (2008). A distributed generative CSP framework for multi-site product configuration. In Klusch, M., Pechoucek, M., & Polleres, A. (Eds.), Cooperative information agents XII (pp 131–146).

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Acknowledgments

We thank anonymous reviewers for their very helpful comments. Financial support through Discovery Grant from NSERC, Canada is acknowledged.

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Correspondence to Yang Xiang.

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This article significantly extends Xiang and Srinivasan [41, 42] as part of the Canadian AI 2013 Proceedings.

Appendix: Proofs

Appendix: Proofs

Proof of Proposition 1

We show that running intersection holds in \(T'\). Let \(C'\) and \(Q'\) be non-adjacent clusters in \(T'\) such that \(C' \cap Q' \not = \emptyset \), and \(X'\) be a cluster on the path between \(C'\) and \(Q'\). Let \(C\), \(Q\), \(X\) be clusters in \(T\) corresponding to \(C'\), \(Q'\), \(X'\), respectively. We show that \(C' \cap Q'\) is contained in \(X'\), namely, \(C' \cap Q' \subseteq X'\).

Since \(T\) is a JT, \(C \cap Q\) is contained in \(X\), i.e., \(C \cap Q \subseteq X\). Because \(C\) and \(Q\) are boundaries (made of shared variables), it follows \(C' \cap Q' = C \cap Q\). That is, \(C' \cap Q' \subseteq X\). Since \(X\) is a boundary, we have \(X' = X \cup Y'\), where \(Y'\) is the set of private variables in subenvironment \(X'\). Hence, \(X \subseteq X'\). From \(C' \cap Q' \subseteq X\) and \(X \subseteq X'\), it follows that \(C' \cap Q' \subseteq X'\). \(\square \)

Proof of Proposition 3

By assumption, the transmission of each message takes at most one time unit. Combining Eqs. (7) and (9) and substituting \(t_4\) by \(t\), the result follows. \(\square \)

Proof of Proposition 5

Suppose that no JT exists with boundaries in \(W\) as clusters, but a JT \(T'\) exists with subenvironments in \(\Omega \) as clusters. Then for every pair of nonadjacent clusters in \(T'\) such that \(C' \cap Q' \not = \emptyset \), and a cluster \(X'\) on the path between \(C'\) and \(Q'\), it holds that \(C' \cap Q' \subseteq X'\).

Let \(T\) be a cluster tree with boundaries in \(W\) as clusters such that it is isomorphic to \(T'\) with each boundary mapped to the corresponding subenvironment. Let \(C\), \(Q\), \(X\) be clusters in \(T\) corresponding to \(C'\), \(Q'\), \(X'\), respectively. Since \(C\) and \(Q\) are boundaries, \(C \cap Q = C' \cap Q' \subseteq X' = X \cup Y'\), where \(Y'\) is the set of private variables in subenvironment \(X'\). From \(C \cap Q \subseteq X \cup Y'\) and \(C \cap Q \cap Y' = \emptyset \), we obtain \(C \cap Q \subseteq X\). That is, running intersection holds in \(T\) and \(T\) is a JT: a contradiction to the assumption. \(\square \)

Proof of Lemma 1

From subcondition 1, a JT \(T\) exists and no two clusters in \(T\) are comparable. We prove the claim by contradiction. Suppose there exists a cluster \(Q\) in \(T\) such that for every boundary \(W_i \in W\), \(Q \not = W_i\).

From subcondition 2, there exists a boundary \(W_i\) such that \(Q \subseteq W_i\). Since \(Q \not = W_i\), it follows that \(Q \subset W_i\). Because \(BG\) is the boundary graph, \(W_i\) is complete in \(BG\). Therefore, there exists a clique \(C_i\) in \(BG\) such that \(W_i \subseteq C_i\). Since clusters of \(T\) are cliques in \(BG\), \(C_i\) must be a cluster in \(T\). From \(Q \subset W_i\) and \(W_i \subseteq C_i\), we have \(Q \subset C_i\). That is, \(T\) contains two comparable clusters: a contradiction. Hence, every cluster in \(T\) is a boundary. \(\square \)

Proof of Theorem 1

[Necessary Condition] Suppose a JT \(H\) exists whose clusters are subenvironments. Extending Proposition 1, if private variables are removed from each cluster, the resultant cluster tree \(T\) is still a JT, whose set of clusters is \(W\). Since \(T\) is a JT, the boundary graph \(BG\) is chordal and subcondition 1 holds. Since each clique of \(BG\) is a cluster in \(T\) and each cluster of \(T\) is a boundary, subcondition 2 follows.

[Sufficient Condition] Suppose both subconditions hold. We prove by construction. Since \(BG\) is chordal, a JT \(T\) exists whose clusters are cliques of \(BG\). By Lemma 1, every cluster in \(T\) is a boundary. Hence, for every cluster \(C\) in \(T\) such that \(C = W_i\) for some \(W_i \in W\), we associate \(C\) with agent \(A_i\).

It is possible that not every agent has been associated with a cluster in \(T\) yet. In that case, consider such an agent \(A_i\) whose boundary is \(W_i\). Since \(W_i\) is complete in \(BG\), there exists a cluster \(C\) in \(T\) such that \(W_i \subseteq C\). Add a new cluster \(W_i\) to \(T\), making it adjacent to cluster \(C\) only, and associate the new cluster with \(A_i\). Repeat this for each remaining agent, until each agent is associated with a cluster in \(T\).

Next, for each agent, add its private variables to its associated cluster in \(T\). The resultant \(T\) is a JT agent organization with each cluster being a subenvironment. \(\square \)

Proof of Theorem 2

Based on Proposition 1, it suffices to prove the theorem relative to a boundary based JT.

[Necessary Condition] Suppose a boundary based JT \(T\) exists whose clusters are boundaries in \(W\). We show that boundaries in \(W\) can be eliminated iteratively using \(T\) as a reference structure.

Since \(T\) is a tree, there exists a leaf cluster \(W_i\). Let \(W_j\) be the adjacent cluster of \(W_i\). Since \(T\) is a JT, for every \(W_k\) such that \(W_k \not = W_i\) and \(W_k \not = W_j\), we have \(W_i \cap W_k \subseteq W_j\). Hence, \(W_i \subseteq W_j\) and \(W_i\) can be eliminated from \(W\) relative to \(W_j\) to obtain \(W'_j\) and the reduced boundary set \(W'\). Cluster \(W_i\) can also be removed from \(T\), with cluster \(W_j\) replaced by \(W'_j\) and resultant cluster tree denoted by \(T'\).

Now the set of clusters of \(T'\) is \(W'\). Since \(W'_j\) differs from \(W_j\) only in terms of variables that \(W_j\) shares uniquely with \(W_i\), \(T'\) is still a JT. Hence, the above operation can be applied repeatedly, until two boundaries are left. Since the reduced \(W'\) is a well-defined boundary set, the two boundaries must be identical and the last elimination results in \(\{\emptyset \}\).

[Sufficient Condition] Suppose \(W\) can be eliminated iteratively into a singleton. Denote the sequence of reduced boundary sets as

$$\begin{aligned} W^{\eta }, W^{\eta -1}, \ldots , W^2, W^1, \end{aligned}$$

where \(\eta \) is the number of agents, \(W^{\eta } = W\), \(W^1 = \{\emptyset \}\), and superscript counts the number of boundaries.

Each \(W^i\) (\(i>1\)) is a well-defined boundary set. The elimination process can be viewed as follows. To produce \(W^x\) from \(W^{x+1}\), eliminate a \(W_i \in W^{x+1}\) relative to a \(W_j \in W^{x+1}\). Remove from \(W_j\) variables that it shares with \(W_i\) but not with any other \(W_k \in W^{x+1}\). The result is \(W^x\). We show below that boundaries in each \(W^x\), for \(x = 2, \ldots , \eta \), can be organized into a JT.

It is trivially true for \(x=2\), as can be seen from Example 9. We assume that it is true for \(x = n\), and consider the case \(x=n+1\). Suppose when \(W^n\) is derived from \(W^{n+1}\), \(W_i \in W^{n+1}\) is eliminated relative to \(W_j \in W^{n+1}\) and \(W_j\) is reduced to \(W'_j \in W^n\). By inductive assumption, boundaries in \(W^n\) can be organized into a JT \(T^n\). If the cluster \(W'_j\) in \(T^n\) is replaced by \(W_j\), the resultant cluster tree is still a JT, since \(W_j{\setminus } W'_j\) is not shared by any other cluster in \(T^n\). Next, add cluster \(W_i\) to \(T^n\), and make it adjacent to \(W_j\). Since \(W_i \subseteq W_j\), the resultant cluster tree is still a JT, and its clusters are exactly the boundaries in \(W^{n+1}\). Hence, boundaries in \(W^{n+1}\) can be organized into a JT.

From the above induction, boundaries in \(W = W^{\eta }\) can be organized into a JT. \(\square \)

Proof of Theorem 3

It suffices to show that the sender-receiver relations define a boundary based JT for \(W\). Assume that \(W\) has a boundary based JT \(T\). Since \(T\) is a cluster tree, there exists a leaf cluster \(W_i \in W\) in \(T\), that is adjacent to a single cluster \(W_j \not = W_i\) in \(T\). Because \(T\) satisfies running intersection, \( W_i \cap W_k \subseteq W_j \) holds for every \(W_k \in W\), where \(k \not = i\). It can be equivalently written as \( W_i \cap W_k \subseteq W_i \cap W_j = I_{ij}\) for every \(W_k \not = W_i\). It follows that

$$\begin{aligned} \bigcup _{k \not = i} (W_i \cap W_k) \subseteq I_{ij}. \end{aligned}$$

Since \(W_i\) is the boundary of \(A_i\), \(\bigcup _{k \not = i} (W_i \cap W_k) = W_i\) and the above becomes \(W_i \subseteq I_{ij}\). As \(I_{ij}\) is the border between \(A_i\) and \(A_j\), we have \(W_i \supseteq I_{ij}\) which yields \(W_i = I_{ij}\).

Let \(Q\) be the set of leaf clusters in \(T\) and \(S\) be the set of links between clusters in \(T\). Let \(T'\) be another boundary based JT of \(W\) with the corresponding \(Q'\) and \(S'\). In general, \(Q \not = Q'\). However, it is always true that \(S = S'\) (see Proposition 8.3 in [35]).

Let \(\mathcal{T}_1\) be the set of all boundary based JTs of \(W\), \(S_1\) be the set of links in any such JT, and \(\mathcal{Q}_1\) be the union of leaf cluster sets over all such JTs (one \(Q\) per JT). Then, after \(HTBS\) starts from a leader agent, the first agent \(A_i\) that runs \(DoDFT\) and passes the test in line 1 must have its boundary \(W_i \in \mathcal{Q}_1\). Suppose \(A_i\) sends the first inter-agent \(StartNewDFT\) to \(A_j\). Hence, the sender-receiver relation \(\langle A_i, A_j \rangle \) identifies the link \(\langle W_i, W_j \rangle \) in \(S_1\).

When \(A_j\) responds to \(StartNewDFT\), \(W_i\) is eliminated and \(W_j\) is updated (as \(Y_j\), Proc. 7, line 5). The reduced boundary set \(W'\), without \(W_i\) and \(W_j\) but with \(Y_j\), is a well-defined boundary set. Hence, \(\mathcal{T}_2\), \(S_2\), and \(\mathcal{Q}_2\) can be defined accordingly from \(W'\), where each \(T \in \mathcal{T}_2\) has one cluster less than \(\mathcal{T}_1\) and \(S_2 = S_1 {\setminus } \{\langle W_i, W_j \rangle \}\) (one link less).

Applying the argument for \(\mathcal{T}_1\) similarly to \(\mathcal{T}_2\), another \(StartNewDFT\) will be sent, resulting in \(\mathcal{T}_3\), \(S_3\), and \(\mathcal{Q}_3\). Since \(|W| = \eta \) and \(|S_1| = \eta - 1\), after \(\eta -1\) \(StartNewDFT\) messages, the reduced boundary set has \(|W'| = 1\) and all links in \(S_1\) are identified, which specifies one of the JTs in \(\mathcal{T}_1\). \(\square \)

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Xiang, Y., Srinivasan, K. Privacy preserving existence recognition and construction of hypertree agent organization. Auton Agent Multi-Agent Syst 30, 220–258 (2016). https://doi.org/10.1007/s10458-015-9285-5

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