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IEEE 36th Computer Security Foundations Symposium (CSF)

作者： Tsoupidi, Rodothea Myrsini Lozano, Roberto Casta Neda Troubitsyna, Elena Papadimitratos, Panagiotis Royal Inst Technol KTH Stockholm Sweden

ISBN: (纸本)9798350321920

Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret information via side channels. Software masking is a mitigation approach against power side-channel attacks aiming at hiding the secret-revealing dependencies from the power footprint of a vulnerable implementation. However, this type of software mitigation often depends on general-purpose compilers, which do not preserve non-functional properties. Moreover, microarchitectural features, such as the memory bus and register reuse, may also leak secret information. These abstractions are not visible at the high-level implementation of the program. Instead, they are decided at compile time. To remedy these problems, security engineers often sacrifice code efficiency by turning off compiler optimization and/or performing local, post-compilation transformations. This paper proposes Secure by Construction Code Generation (SecCG), a constraint-based compiler approach that generates optimized yet protected against power side channels code. SecCG controls the quality of the mitigated program by efficiently searching the best possible low-level implementation according to a processor cost model. In our experiments with twelve masked cryptographic functions up to 100 lines of code on Mips32 and ARM Thumb, SecCG speeds up the generated code from 77% to 6.6 times compared to non-optimized secure code with an overhead of up to 13% compared to non-secure optimized code at the expense of a high compilation cost. For security and compiler researchers, this paper proposes a formal model to generate power side channel free low-level code. For software engineers, SecCG provides a practical approach to optimize performance critical and vulnerable cryptographic implementations that preserve security properties against power side channels.

关键词： compilation power side-channel attacks code optimization software masking constraint programming

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A new branch-and-filter exact algorithm for binary constraint satisfaction problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期299卷 448-467页

作者： San Segundo, Pablo Furini, Fabio Leon, Rafael Univ Politecn Madrid UPM Ctr Automat & Robot CAR Madrid Spain Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Ruber Rome Italy

A binary constraint satisfaction problem (BCSP) consists in determining an assignment of values to variables that is compatible with a set of constraints. The problem is called binary because the constraints involve only pairs of variables. The BCSP is a cornerstone problem in constraint programming (CP), appearing in a very wide range of real-world applications. In this work, we develop a new exact algorithm which effectively solves the BCSP by reformulating it as a k-clique problem on the underlying microstructure graph representation. Our new algorithm exploits the cutting-edge branching scheme of the stateof-the-art maximum clique algorithms combined with two filtering phases in which the domains of the variables are reduced. Our filtering phases are based on colouring techniques and on heuristically solving an associated boolean satisfiability (SAT) problem. In addition, the algorithm initialization phase performs a reordering of the microstructure graph vertices that produces an often easier reformulation to solve. We carry out an extensive computational campaign on a benchmark of almost 20 0 0 instances, encompassing numerous real and synthetic problems from the literature. The performance of the new algorithm is compared against four SAT-based solvers and three general purpose CP solvers. Our tests reveal that the new algorithm significantly outperforms all the others in several classes of BCSP instances. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Combinatorial optimization Binary constraint satisfaction problems constraint programming Exact algorithm Computational experiments

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Modelling and solving the supply marketing order allocation problem with time consistency and bundle discounts

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2022年第8期73卷 1682-1691页

作者： Zhou, Yupeng Liu, Minghao Ma, Feifei Luo, Na Yin, Minghao Northeast Normal Univ Changchun Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

The optimizations with respect to the supply chain management have far-reaching effects on enterprise development as well as people's livelihood. By this fact, it is important to solve the order allocation problem in a real scenario. We focus on optimising a supply marketing order allocation problem (SMOAP) with various constraints from an algorithmic aspect. The SMOAP considers how to match the orders between purchasers and suppliers to maximise the platform's benefits. We first formulate it as a constrained optimisation problem and then prove that SMOAP is NP-hard. The formulation describes a new trading mode with bundle discounts, time consistency and transportation cost in a real scenario. Based on this trading mode, satisfiability modulo theories (SMT) and constraint programming (CP) optimisers are performed to gain efficient solutions. Moreover, we further propose a tabu search (TS) with two-level perturbation mechanism, score-based heuristic and preprocessing techniques to search for promising solutions. The efficiency of our approaches is confirmed by experiments on real data instances from an electronic commerce trading platform in Jiutai district of Changchun city, Jilin province, China. Experimental results reveal that the proposed method is quite effective.

关键词： Supply marketing order allocation electronic commerce trading platform satisfiability modulo theories constraint programming tabu search

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Time allocation and long term scheduling of ESO telescopes at La Silla Paranal Observatory 10

Time allocation and long term scheduling of ESO telescopes a...

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Observatory Operations: Strategies, Processes, and Systems X 2024

作者： Rejkuba, M. Hainaut, O.R. Bierwirth, T. Pruemm, M. Weiss, A. European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching b. München85748 Germany Fev Etamax GmbH Germany

ISBN: (纸本)9781510675193

The preparation of the long-term telescope schedule follows the submission and scientific review of new proposals. At the European Southern Observatory (ESO) this process entails scheduling the scientific proposals according to their scientific merit and available observing resources, as well as scheduling technical, maintenance, and commissioning activities for all operational telescopes of the La Silla Paranal Observatory. After the recent overhaul of the phase one proposal submission software, ESO started the development of a new telescopes time allocation and scheduling tool. The new tool makes the scheduling process more efficient while optimizing the use of observing facilities. Besides scheduling activities and allocating time according to scientific merit, available resources, operational and programmatic needs, the tool will enable simultaneous scheduling of multiple telescopes to appropriately account for dependencies between them. The implementation of this new Time Allocation tool opens the possibility for dynamic re-scheduling of the telescopes, which is a pre-requisite to implementing a yearly "Call for Proposals"along with the "Fast Track Channel"at ESO. © 2024 SPIE.

关键词： constraint programming

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Comparing Optimization Methods for Radiation Therapy Patient Scheduling using Different Objectives

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Operations Research Forum 2023年第4期4卷 83页

作者： Frimodig, Sara Enqvist, Per Carlsson, Mats Mercier, Carole Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden RaySearch Laboratories Stockholm Sweden Department of Computer Science RISE Research Institutes of Sweden Gothenburg Sweden Department of Radiation Oncology Iridium Netwerk Antwerp Belgium

Radiation therapy (RT) is a medical treatment to kill cancer cells or shrink tumors. To manually schedule patients for RT is a time-consuming and challenging task. By the use of optimization, patient schedules for RT can be created automatically. This paper presents a study of different optimization methods for modeling and solving the RT patient scheduling problem, which can be used as decision support when implementing an automatic scheduling algorithm in practice. We introduce an Integer programming (IP) model, a column generation IP model (CG-IP), and a constraint programming model. Patients are scheduled on multiple machine types considering their priority for treatment, session duration and allowed machines. Expected future arrivals of urgent patients are included in the models as placeholder patients. Since different cancer centers can have different scheduling objectives, the models are compared using multiple objective functions, including minimizing waiting times, and maximizing the fulfillment of patients’ preferences for treatment times. The test data is generated from historical data from Iridium Netwerk, Belgium’s largest cancer center with 10 linear accelerators. The results demonstrate that the CG-IP model can solve all the different problem instances to a mean optimality gap of less than 1 % within one hour. The proposed methodology provides a tool for automated scheduling of RT treatments and can be generally applied to RT centers. © 2023, The Author(s).

关键词： Column generation constraint programming Integer programming Patient scheduling Radiation therapy

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Nurse rostering with fatigue modelling Incorporating a validated sleep model with biological variations in nurse rostering

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HEALTH CARE MANAGEMENT SCIENCE 2023年第1期26卷 21-45页

作者： Klyve, Kjartan Kastet Senthooran, Ilankaikone Wallace, Mark Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Trondheim Norway Monash Univ Melbourne Vic Australia

We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer programming and constraint programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses' biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.

关键词： Nurse rostering Fatigue Sleep Mixed-Integer programming constraint programming Large neighbourhood search Operations research

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constraint Solving Approaches to the Business-to-Business Meeting Scheduling Problem 32

Constraint Solving Approaches to the Business-to-Business Me...

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32nd International Joint Conference on Artificial Intelligence (IJCAI)

作者： Bofill, Miquel Coll, Jordi Garcia, Marc Giraldez-Cru, Jesus Pesant, Gilles Suy, Josep Villaret, Mateu Univ Girona IMAE Dept Girona Spain CSIC Artificial Intelligence Res Inst IIIA Bellaterra Spain Univ Granada DaSCI Inst DECSAI Granada Spain Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9781956792034

The B2B Meeting Scheduling Optimization Problem (B2BSP) consists of scheduling a set of meetings between given pairs of participants to an event, minimizing idle time periods in participants' schedules, while taking into account participants' availability and accommodation capacity. Therefore, it constitutes a challenging combinatorial problem in many real-world B2B events. This work presents a comparative study of several approaches to solve this problem. They are based on constraint programming (CP), Mixed Integer programming (MIP) and Maximum Satisfiability (MaxSAT). The CP approach relies on using global constraints and has been implemented in MiniZinc to be able to compare CP, Lazy Clause Generation and MIP as solving technologies in this setting. A pure MIP encoding is also presented. Finally, an alternative viewpoint is considered under MaxSAT, showing the best performance when considering some implied constraints. Experimental results on real world B2B instances, as well as on crafted ones, show that the MaxSAT approach is the one with the best performance for this problem, exhibiting better solving times, sometimes even orders of magnitude smaller than CP and MIP.

关键词： constraint programming

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Exploiting Fairness to Enhance Sensitive Attributes Reconstruction

Exploiting Fairness to Enhance Sensitive Attributes Reconstr...

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1st IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

作者： Ferry, Julien Aivodji, Ulrich Gambs, Sebastien Huguet, Marie-Jose Siala, Mohamed Univ Toulouse CNRS LAAS Toulouse France Ecole Technol Superieure Montreal PQ Canada Univ Quebec Montreal Montreal PQ Canada Univ Toulouse CNRS LAAS INSA Toulouse France

ISBN: (纸本)9781665462990

In recent years, a growing body of work has emerged on how to learn machine learning models under fairness constraints, often expressed with respect to some sensitive attributes. In this work, we consider the setting in which an adversary has black-box access to a target model and show that information about this model's fairness can be exploited by the adversary to enhance his reconstruction of the sensitive attributes of the training data. More precisely, we propose a generic reconstruction correction method, which takes as input an initial guess made by the adversary and corrects it to comply with some user-defined constraints (such as the fairness information) while minimizing the changes in the adversary's guess. The proposed method is agnostic to the type of target model, the fairness-aware learning method as well as the auxiliary knowledge of the adversary. To assess the applicability of our approach, we have conducted a thorough experimental evaluation on two state-of-the-art fair learning methods, using four different fairness metrics with a wide range of tolerances and with three datasets of diverse sizes and sensitive attributes. The experimental results demonstrate the effectiveness of the proposed approach to improve the reconstruction of the sensitive attributes of the training set.

关键词： Reconstruction attack privacy fairness machine learning constraint programming

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Binary Search-Based Methods for Solving constraint Hierarchies over Finite Domains 35

Binary Search-Based Methods for Solving Constraint Hierarchi...

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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Hosobe, Hiroshi Satoh, Ken Hosei Univ Fac Comp & Informat Sci Tokyo Japan Natl Inst Informat Principles Informat Res Div Tokyo Japan

ISBN: (纸本)9798350342734

constraint programming is a powerful tool for modeling and solving various problems. Especially, soft constraints are useful since they enable the treatment of over- and under-constrained real-world problems by relaxing conflicting constraints and introducing default constraints. constraint hierarchies provide a soft constraint framework that introduces hierarchical preferences called strengths. In a constraint hierarchy, constraints are associated with strengths such as required, strong, medium, and weak, and a solution is obtained to maximally satisfy stronger constraints in the sense of a given solution criterion. In this paper, we propose three methods based on binary search for solving constraint hierarchies over finite domains by using a criterion called unsatisfied-count-better. Our methods solve constraint hierarchies by encoding them into ordinary constraint satisfaction problems and repeatedly solving the encoded problems with an external solver. We also present the implementations of our methods and the results of the experiment that we conducted to evaluate them.

关键词： constraint programming constraint solver soft constraint constraint hierarchy

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Cutting the Cake into Crumbs: Verifying Envy-Free Cake-Cutting Protocols Using Bounded Integer Arithmetic 26th

Cutting the Cake into Crumbs: Verifying Envy-Free Cake-Cutti...

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26th International Symposium on Practical Aspects of Declarative Languages (PADL)

作者： Lester, Martin Mariusz Univ Reading Reading Berks England

ISBN: (纸本)9783031520372;9783031520389

Fair division protocols specify how to split a continuous resource (conventionally represented by a cake) between multiple agents with different preferences. Envy-free protocols ensure no agent prefers any other agent's allocation to his own. These protocols are complex and manual proofs of their correctness may contain errors. Recently, Bertram and others [5] developed the DSL Slice for describing these protocols and showed how verification of envy-freeness can be reduced to SMT instances in the theory of quantified non-linear real arithmetic. This theory is decidable, but the decision procedure is slow, both in theory and in practice. We prove that, under reasonable assumptions about the primitive operations used in the protocol, counterexamples to envy-freeness can always be found with bounded integer arithmetic. Building on this result, we construct an embedded DSL for describing cake-cutting protocols in declarative-style C. Using the bounded model-checker CBMC, we reduce verifying envy-freeness of a protocol to checking unsatisfiability of pure SAT instances. This leads to a substantial reduction in verification time when the protocol is unfair.

关键词： fair division constraint programming declarative C

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