A look ahead approach to secure multi-party protocols

Secure multi-party protocols have been proposed to enable non-colluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. Therefore, the high overhead makes it necessary for parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look ahead approach, specifically for secure multi-party protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. Look ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods

Ensuring location diversity in privacy preserving spatio-temporal data mining

The rise of mobile technologies in the last decade has lead to vast amounts of location information generated by individuals. From the knowledge discovery point of view, this data is quite valuable as it has commercial value, but the inherent personal information in the data raises privacy concerns. There exist many algorithms in the literature to satisfy the privacy requirements of individuals, by generalizing, perturbing, and suppressing data. The algorithms that try to ensure a level of indistinguishability between trajectories in the dataset, fail when there is not enough diversity among sensitive locations visited by those users. We propose an approach that ensures location diversity named as (c,p)- confidentiality, which bounds the probability of visiting a sensitive location given the background knowledge of the adversary. Instead of grouping the trajectories, we anonymize the underlying map structure. We explain our algorithm and show the performance of our approach. We also compare the performance of our algorithm with an existing technique and show that location diversity can be satisfied efficiently

A look-ahead approach to secure multiparty protocols

Secure multiparty protocols have been proposed to enable noncolluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. The high overhead motivates parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look-ahead approach, specifically for secure multiparty protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. The look-ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods

Operational variable job scheduling with eligibility constraints: a randomized constraint-graph-based approach

In this study, we consider the problem of Operational Variable Job Scheduling, also referred to as parallel machine scheduling with time windows. The problem is a more general version of the Fixed Job Scheduling problem, involving a Lime window for each job larger than its processing time. The objective is to find the optimal subset of the jobs that can be processed. An interesting application area ties in Optimal Berth Allocation, which involves the assignment of vessels arriving at the port to appropriate berths within their time windows, while maximizing the total profit from the served vessels. Eligibility constraints are also taken into consideration. We develop an integer programming model for the problem. We show that the problem is NP-hard, and develop a constraint-graph-based construction algorithm for generating near-optimal solutions. We use genetic algorithm and other improvement algorithms to enhance the solution. Computational experimentation reveals that our algorithm generates very high quality solutions in very small computation times