Searching for joint gains in automated negotiations based on multi-criteria decision making theory

It is well established by conflict theorists and others that successful negotiation should incorporate "creating value" as well as "claiming value." Joint improvements that bring benefits to all parties can be realised by (i) identifying attributes that are not of direct conflict between the parties, (ii) tradeoffs on attributes that are valued differently by different parties, and (iii) searching for values within attributes that could bring more gains to one party while not incurring too much loss on the other party. In this paper we propose an approach for maximising joint gains in automated negotiations by formulating the negotiation problem as a multi-criteria decision making problem and taking advantage of several optimisation techniques introduced by operations researchers and conflict theorists. We use a mediator to protect the negotiating parties from unnecessary disclosure of information to their opponent, while also allowing an objective calculation of maximum joint gains. We separate out attributes that take a finite set of values (simple attributes) from those with continuous values, and we show that for simple attributes, the mediator can determine the Pareto-optimal values. In addition we show that if none of the simple attributes strongly dominates the other simple attributes, then truth telling is an equilibrium strategy for negotiators during the optimisation of simple attributes. We also describe an approach for improving joint gains on non-simple attributes, by moving the parties in a series of steps, towards the Pareto-optimal frontier

Towards Privacy-, Budget-, and Deadline-Aware Service Optimization for Large Medical Image Processing across Hybrid Clouds

Efficiently processing medical images, such as whole slide images in digital pathology, is essential for timely diagnosing high-risk diseases. However, this demands advanced computing infrastructure, e.g., GPU servers for deep learning inferencing, and local processing is time-consuming and costly. Besides, privacy concerns further complicate the employment of remote cloud infrastructures. While previous research has explored privacy and security-aware workflow scheduling in hybrid clouds for distributed processing, privacy-preserving data splitting, optimizing the service allocation of outsourcing computation on split data to the cloud, and privacy evaluation for large medical images still need to be addressed. This study focuses on tailoring a virtual infrastructure within a hybrid cloud environment and scheduling the image processing services while preserving privacy. We aim to minimize the use of untrusted nodes, lower monetary costs, and reduce execution time under privacy, budget, and deadline requirements. We consider a two-phase solution and develop 1) a privacy-preserving data splitting algorithm and 2) a greedy Pareto front-based algorithm for optimizing the service allocation. We conducted experiments with real and simulated data to validate and compare our method with a baseline. The results show that our privacy mechanism design outperforms the baseline regarding the average lower band on individual privacy and information gain for privacy evaluation. In addition, our approach can obtain various Pareto optimal-based allocations with users' preferences on the maximum number of untrusted nodes, budget, and time threshold. Our solutions often dominate the baseline's solution and are superior on a tight budget. Specifically, our approach has been ahead of baseline, up to 85.2% and 6.8% in terms of the total financial and time costs, respectively

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain,however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P3 system deployed over our SmartLab4 testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors

Theoretical Computer Science and Discrete Mathematics

This book includes 15 articles published in the Special Issue "Theoretical Computer Science and Discrete Mathematics" of Symmetry (ISSN 2073-8994). This Special Issue is devoted to original and significant contributions to theoretical computer science and discrete mathematics. The aim was to bring together research papers linking different areas of discrete mathematics and theoretical computer science, as well as applications of discrete mathematics to other areas of science and technology. The Special Issue covers topics in discrete mathematics including (but not limited to) graph theory, cryptography, numerical semigroups, discrete optimization, algorithms, and complexity

On the Provision of Public Goods on Networks: Incentives, Exit Equilibrium, and Applications to Cyber .

Attempts to improve the state of cyber-security have been on the rise over the past years. The importance of incentivizing better security decisions by users in the current landscape is two-fold: it not only helps users protect themselves against attacks, but also provides positive externalities to others interacting with them, as a protected user is less likely to become compromised and be used to propagate attacks against other entities. Therefore, security can be viewed as a public good. This thesis takes a game-theoretic approach to understanding the theoretical underpinnings of users' incentives in the provision of public goods, and in particular, cyber-security. We analyze the strategic interactions of users in the provision of security as a non-excludable public good. We propose the notion of exit equilibrium to describe users' outside options from mechanisms for incentivizing the adoption of better security decisions, and use it to highlight the crucial effect of outside options on the design of incentive mechanisms for improving the state of cyber-security. We further focus on the general problem of public good provision games on networks. We identify necessary and sufficient conditions on the structure of the network for the existence and uniqueness of the Nash equilibrium in these games. We show that previous results in the literature can be recovered as special cases of our result. We provide a graph-theoretical interpretation of users' efforts at the Nash equilibria, Pareto efficient outcomes, and semi-cooperative equilibria of these games, by linking users' effort decisions to their centralities in the interaction network. Using this characterization, we separate the effects of users' dependencies and influences (outgoing and incoming edges, respectively) on their effort levels, and uncover an alternating effect over walks of different length in the network. We also propose the design of inter-temporal incentives in a particular type of security games, namely, security information sharing agreement. We show that either public or private assessments can be used in designing incentives for participants to disclose their information in these agreements. Finally, we present a method for crowdsourcing reputation that can be useful in attaining assessments of users' efforts in security games.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133328/1/naghizad_1.pd