Association rule hiding using integer linear programming

<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

Mathematical Modeling of Ballistic Missile Defense

Today, many countries are looking for missile strikes to achieve their goals. To optimize ballistic missile defense, missile defense centers identify potential launch points for enemy missiles to anticipate enemy attacks to reduce potential damage. One of these measures is mathematical modeling for the scenario of a possible enemy attack and defensive cover against this attack. In this research, mathematical optimization and mixed integer linear program have been used to reduce the damage against the enemy attack. The purpose of this study is to minimize the maximum damage caused by enemy missile attacks.

Regular Expression Learning from Positive Examples Based on Integer Programming

This paper presents a novel method to infer regular expressions from positive examples. The method consists of a candidate’s construction phase and an optimization phase. We first propose multiscaling sample augmentation to capture the cycle patterns from single examples during the candidate’s construction phase. We then use common substrings to build regular expressions that capture patterns across multiple examples, and we show this algorithm is more general than those based on common prefixes or suffixes. Furthermore, we propose a pruning mechanism to improve the efficiency of useful common substring mining, which is an important part of common substring-based expression building algorithm. Finally, in the optimization phase, we model the problem of choosing a set of regular expressions with the lowest cost as an integer linear program, which can be solved to obtain the optimal solution. The experimental results on synthetic and real-life samples demonstrate the effectiveness of our approach in inferring concise and semantically meaningful regular expressions for string datasets.

Optimizing pricing and packing of variable-sized cargo

Organizations have successfully used dynamic pricing to optimize revenues for many years, where research and practice have mainly focused on applications with independent, discrete commodities; for example, an airline ticket. In this research we consider applications where the commodity is continuous and the value of the commodity available to sell depends on the combination of previously accepted demand. We focus on vehicle ferries, where the accepted vehicle bookings are packed in lanes in the ferry to leave a usable space for future bookings. Certain combinations of vehicles may result in areas of unusable space, which will affect future revenue. While this application is the focus of the paper, there are numerous industries that face similar challenges including freight and the sale of advertising on television and radio. In this paper, we simultaneously solve the pricing and resource utilization problem to optimality for a discrete set of product types and stochastic demand. Our approach combines a dynamic pricing model with a mixed-integer linear program to optimize the packing. We present results for real-world examples from the ferry industry and discuss extensions to the method to improve the selection of vehicle configurations.

Capacitated Refuge Assignment for Speedy and Reliable Evacuation

When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not taken into account. In this paper, we propose a refuge assignment scheme that considers both the speediness and safety of evacuation under the refuge capacity constraint. We first formulate the refuge assignment problem as a two-step integer linear program (ILP). Since the two-step ILP requires route candidates between evacuees and their possible refuges, we further propose a speedy and reliable route selection scheme as an extension of the existing route selection scheme. Through numerical results using the actual data of Arako district of Nagoya city in Japan, we show that the proposed scheme can improve the average route reliability among evacuees by 13.6% while suppressing the increase of the average route length among evacuees by 7.3%, compared with the distance-based route selection and refuge assignment. In addition, we also reveal that the current refuge capacity is not enough to support speedy and reliable evacuation for the residents.