ABKS-SKGA: Attribute-based keyword search secure against keyword guessing attack

In order to guarantee security and privacy of sensitive data, attribute-based keyword search (ABKS) enables data owners to upload their encrypted data to cloud servers, and authorizes intended data users to retrieve it. Meanwhile, ABKS outsources heavy search work to cloud servers, which makes ABKS adaptive to mobile computing environment. However, as cloud servers can both generate keyword ciphertexts and run search algorithm, the existing most ABKS schemes are vulnerable to keyword guessing attack. In this paper, we show the fundamental cause that the existing ABKS schemes do not resist keyword guessing attack is any entity can generate keyword ciphertext. To solve the above problem, in the phase of keyword ciphertext generation, we use private key of data owner to sign keyword prior to generating keyword ciphertext. Therefore, any other entity does not forge keyword ciphertext, which can resist keyword guessing attack. We give the formal definition and security model of attributed-based keyword search secure against keyword guessing attack (ABKS-SKGA). Furthermore, we provide an ABKS-SKGA scheme. The ABKS-SKGA scheme is proved secure against chosen-plaintext attack (CPA). Performance analysis shows that the proposed scheme is practical.     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Detecting criminal organizations in mobile phone networks

The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as mobile phones and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first we provide a theoretical framework for the problem of detecting and characterizing criminal organizations in networks reconstructed from phone call records. Then, we introduce an expert system to support law enforcement agencies in the task of unveiling the underlying structure of criminal networks hidden in communication data. This platform allows for statistical network analysis, community detection and visual exploration of mobile phone network data. It enables forensic investigators to deeply understand hierarchies within criminal organizations, discovering members who play central role and provide connection among sub-groups. Our work concludes illustrating the adoption of our computational framework for a real-word criminal investigation.     

A general transformation from KP-ABE to searchable encryption

Users are inclined to share sensitive data in a remote server if no strong security mechanism is in place. Searchable encryption satisfies the need of users to execute a search encrypted data. Previous searchable encryption methods such as “public key encryption with keyword search (PEKS)” restricted the data access to certain users, because only the assigned users were able to search the encrypted data. In this paper we will discuss the relation between Attribute Based Encryption (ABE) and searchable encryption and define a weak anonymity of the ABE scheme, named “attribute privacy”. With this weak anonymity, we propose a general transformation from ABE to Attribute Based Encryption with Keyword Search (ABEKS) and a concrete attribute private key-policy ABE (KP-ABE) scheme. We present an ABEKS scheme based on this KP-ABE scheme and permit multi-users to execute a flexible search on the remote encrypted data.     

Window annealing for pixel-labeling problems

The pixel labeling problems in computer vision are often formulated as energy minimization tasks. Algorithms such as graph cuts and belief propagation are prominent; however, they are only applicable for specific energy forms. For general optimization, Markov Chain Monte Carlo (MCMC) based simulated annealing can estimate the minima states very slowly.This paper presents a sampling paradigm for faster optimization. First, in contrast to previous MCMCs, the role of detailed balance constraint is eliminated. The reversible Markov chain jumps are essential for sampling an arbitrary posterior distribution, but they are not essential for optimization tasks. This allows a computationally simple window cluster sample. Second, the proposal states are generated from combined sets of local minima which achieve a substantial increase in speed compared to uniformly labeled cluster proposals. Third, under the coarse-to-fine strategy, the maximum window size variable is incorporated along with the temperature variable during simulated annealing. The proposed window annealing is experimentally shown to be many times faster and capable of finding lower energy compared to the previous Gibbs and Swendsen-Wang cut (SW-cut) sampler. In addition, the proposed method is compared with other deterministic algorithms like graph cut, belief propagation, and spectral method in their own specific energy forms. Window annealing displays competitive performance in all domains.     

Principles and performance analysis of SeCond: A system for epidemic peer-to-peer content distribution

We propose and design a peer-to-peer system, SeCond, addressing the distribution of large sized content to a large number of end systems in an efficient manner. In contrast to prior work, it employs a self-organizing epidemic dissemination scheme for state propagation of available blocks and initiation of block transmissions. In order to exploit heterogeneity of peers, enhance the utilization of system resources and for the ease of deployment, scalability, and adaptivity to dynamic peer arrivals/departures, we propose mechanisms for adjusting protocol parameters dynamically according to the bandwidth usages. We describe design and analysis details of our protocol SeCond. Comprehensive performance evaluations and comparison with the BitTorrent system model have been accomplished for a wide range of scenarios. Performance results include scalability analysis for different arrival/departure patterns, flash-crowd scenario, overhead analysis, and fairness ratio. The major metrics we study include the average file download time, load on the primary seed, uplink/downlink utilization, and communication overhead. We show that SeCond is a scalable and adaptive protocol which takes the heterogeneity of the peers into account. The protocol is as fair as BitTorrent although it has no explicit strategy addressing free-riding. We also illustrate the applicability of an analytical fluid model to the behavior of SeCond.     

A strategic classification support system for brownfield redevelopment

Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets.     

Financial time-series analysis with rough sets

We investigate the use of the rough set model for financial time-series data analysis and forecasting. The rough set model is an emerging technique for dealing with vagueness and uncertainty in data. It has many advantages over other techniques, such as fuzzy sets and neural networks, including attribute reduction and variable partitioning of data. These characteristics can be very useful for improving the quality of results from data analysis. We demonstrate a rough set data analysis model for the discovery of decision rules from time series data for example, the New Zealand stock exchanges. Rules are generated through reducts and can be used for future prediction. A unique ranking system for the decision rules based both on strength of the rule and stability of the rule is used in this study. The ranking system gives the user confidence regarding their market decisions. Our experiment results indicate that the forecasting of future stock index values using rough sets obtains decision ruleswith high accuracy and coverage.     

Achieving sustainable customer loyalty in airports: The role of waiting time satisfaction and self-service technologies

Waiting time appears to be an unavoidable part of the service industry, particularly at the airport, where you may encounter delays due to check-in, screening, and other activities. This waiting experience can vex customers, affecting their perception of the service provider and, consequently, their loyalty. Our study aimed to determine the effect of waiting time satisfaction and the use of self-service technology on the long-term sustainability of customer loyalty. 750 structured questionnaires were distributed to travelers at two international airports in Turkey. PLS-SEM was used to analyses the models. Our findings indicate that customer satisfaction with waiting times and the use of self-service technologies are critical for the long-term sustainability of customer loyalty. Additionally, we discovered that waiting time satisfaction partially mediates the relationship between self-service technology use and long-term customer loyalty. Finally, the managerial implications were discussed, including future research suggestions.