Alberto Ceselli

Modeling and assessing inference exposure in encrypted databases

The scope and character of todays computing environments are progressively shifting from traditional, one-on-one client-server interaction to the new cooperative paradigm. It then becomes of primary importance to provide means of protecting the secrecy of the information, while guaranteeing its availability to legitimate clients. Operating online querying services securely on open networks is very difficult; therefore many enterprises outsource their data center operations to external application service providers. A promising direction toward prevention of unauthorized access to outsourced data is represented by encryption. However, data encryption is often supported for the sole purpose of protecting the data in storage while allowing access to plaintext values by the server, which decrypts data for query execution. In this paper, we present a simple yet robust single-server solution for remote querying of encrypted databases on external servers. Our approach is based on the use of indexing information attached to the encrypted database, which can be used by the server to select the data to be returned in response to a query without the need of accessing the plaintext database content. Our indexes balance the trade-off between efficiency requirements in query execution and protection requirements due to possible inference attacks exploiting indexing information. We investigate quantitative measures to model inference exposure and provide some related experimental results.

A branch-and-price algorithm for the capacitatedp-median problem

The capacitated p-median problem is the variation of the well-known p-median problem in which a demand is associated to each user, a capacity is associated to each candidate median, and the total demand of the users associated to the same median must not exceed its capacity. We present a branch-and-price algorithm, that exploits column generation, heuristics and branchand-bound to compute optimal solutions. We compare our branch-and-price algorithm with other methods proposed so far, and we present computational results both on test instances taken from the literature and on random instances with different values of the ratio between the number of medians and the number of users.

Two exact algorithms for the capacitated p-median problem.

Abstract.The p-median problem has been widely studied in combinatorial optimisation, but its generalisation to the capacitated case has not. We propose a branch and price algorithm, comparing it with a standard MIP solver and a branch and bound algorithm based on Lagrangean relaxation. We present computational experience, using test instances drawn from the literature and new instances with higher ratio between the number of medians p and the number of nodes N. The branch and price algorithm shows very good performances and computational time robustness in solving problems for any

Efficient algorithms for the double traveling salesman problem with multiple stacks

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A branch-and-price algorithm for the multi-depot heterogeneous-fleet pickup and delivery problem with soft time windows

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Optimizing the Cargo Express Service of Swiss Federal Railways

In this paper we investigate theoretical properties of the Double Traveling Salesman Problem with Multiple Stacks. In particular, we provide polynomial time algorithms for different subproblems when the stack size limit is relaxed. Since these algorithms can represent building blocks for more complex methods, we also include them in a simple heuristic which we test experimentally. We finally analyze the impact of handling the stack size limit, and we propose repair procedures. The theoretical investigation highlights interesting structural properties of the problem, and our computational results show that the single components of the heuristic can be successfully incorporated in more complex algorithms or bounding techniques.

A Branch-and-Price Algorithm for the Multilevel Generalized Assignment Problem

The growing cost of transportation and distribution pushes companies, especially small and medium transportation enterprises, to form partnership and to exploit economies of scale. On the other hand, to increase their competitiveness on the market, companies are asked to consider preferences of the customers as well. Therefore, tools for logistics management need to manage collective resources, as many depots and heterogeneous fleets, providing flexible preference handling at the same time. In this paper we tackle a pickup and delivery vehicle routing problem involving such aspects; customers place preferences on visiting time (represented as soft time windows), and their violation is allowed at a price. Our interest in this problem stems from an ongoing industrial project. First we propose an exact branch-and-price algorithm, having as a core advanced dynamic programming techniques. Then we analyze through a computational campaign the impact of soft time windows management on the optimal solution in terms of both routing and overall distribution costs. Our experiments show that our approach can solve instances of real size, and clarify the practical usefulness of soft time windows management.

A computational evaluation of a general branch-and-price framework for capacitated network location problems

The Cargo Express service of Swiss Federal Railways (SBB Cargo) offers fast overnight transportation of goods between selected train stations in Switzerland and is operated as a hub-and-spoke system with two hubs. We present three different models for planning the operation of this service as a whole. All models capture the underlying optimization problem with a high level of detail: Traffic routing, train routing, makeup, scheduling, and locomotive assignment are all addressed. At the same time we respect hard constraints like tight service time windows and train capacities, and we avoid hub overloading. We describe our approaches for obtaining provably good quality solutions. Our algorithmic techniques involve branch-and-cut, branch-and-price, and problem-specific exact and heuristic acceleration methods. We conclude our study with computational results on realistic data.

Mathematical programming algorithms for bin packing problems with item fragmentation

The multilevel generalized assignment problem (MGAP) is a variation of the generalized assignment problem, in which agents can execute tasks at different efficiency levels with different costs. We present a branch-and-price algorithm that is the first exact algorithm for the MGAP. It is based on a decomposition into a master problem with set-partitioning constraints and a pricing subproblem that is a multiple-choice knapsack problem. We report on our computational experience with randomly generated instances with different numbers of agents, tasks, and levels; and with different correlations between cost and resource consumption for each agent-task-level assignment. Experimental results show that our algorithm is able to solve instances larger than those of the maximum size considered in the literature to proven optimality.

A molecular dynamics study of structure, stability and fragmentation patterns of sodium bis(2-ethylhexyl)sulfosuccinate positively charged aggregates in vacuo

The purpose of this paper is to illustrate a general framework for network location problems, based on column generation and branch-and-price. In particular we consider capacitated network location problems with single-source constraints. We consider several different network location models, by combining cardinality constraints, fixed costs, concentrator restrictions and regional constraints. Our general branch-and-price-based approach can be seen as a natural counterpart of the branch-and-cut-based commercial ILP solvers, with the advantage of exploiting the tightness of the lower bound provided by the set partitioning reformulation of network location problems. Branch-and-price and branch-and-cut are compared through an extensive set of experimental tests.