Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature

Protecting private information for two classes of aggregated database queries

An important direction of informatics is devoted to the protection of privacy of confidential information while providing answers to aggregated queries that can be used for analysis of data. Protecting privacy is especially important when aggregated queries are used to combine personal information stored in several databases that belong to different owners or come from different sources. Malicious attackers may be able to infer confidential information even from aggregated numerical values returned as answers to queries over large collections of data. Formal proofs of security guarantees are important, because they can be used for implementing practical systems protecting privacy and providing answers to aggregated queries. The investigation of formal conditions which guarantee protection of private information against inference attacks originates from a fundamental result obtained by Chin and Ozsoyoglu in 1982 for linear queries. The present paper solves similar problems for two new classes of aggregated nonlinear queries. We obtain complete descriptions of conditions, which guarantee the protection of privacy of confidential information against certain possible inference attacks, if a collection of queries of this type are answered. Rigorous formal security proofs are given which guarantee that the conditions obtained ensure the preservation of privacy of confidential data. In addition, we give necessary and sufficient conditions for the protection of confidential information from special inference attacks aimed at achieving a group compromise

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

Establishing reasoning communities of security experts for internet commerce security

The highly sophisticated and rapidly evolving area of internet commerce security presents many novel challenges for the organization of discourse in reasoning communities. This chapter suggests appropriate reasoning methods and demonstrates how establishing reasoning communities of security experts and enabling productive group discourse among them can play a crucial role in successful resolution of problems concerning the implementation, integration, deployment and maintenance of flexible local security systems for defense against malware threats in internet security. Local security systems of this sort may combine several ready open source or commercial software packages behind a common front-end and may enhance and supplement their facilities with additional plug-ins. To illustrate the diverse character of challenges the reasoning communities in internet security are likely to be faced with, this chapter concentrates on defense against phishing attacks. This example was selected as it is one of the newest and most rapidly changing application domains for the principles of organizing reasoning communities. The major group discourse methods suggested for the reasoning communities of security experts in this chapter include the Delphi Method, the Wideband Delphi Process, the Generic/Actual Argument Model of Structured Reasoning, Brainstorming, Reverse Brainstorming, Consensus Decision Making, Voting, Open Delphi and Open Brainstorming Methods. The Delphi Method and Wideband Delphi Process are suggested as tools for organizing a cohesive reasoning architecture, for coordinating other methods, and for preparing and allocating other methods to particular issues

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

A multistage protocol for aggregated queries in distributed cloud databases with privacy protection

This article is devoted to the novel situation, where a large distributed cloud database is a union of several separate databases belonging to individual database owners who are not allowed to transfer their data for storage in locations different from their already chosen separate cloud service providers. For example, a very large number of medical records may be stored in a distributed cloud database, which is a union of several separate databases from different hospitals, or even from different countries. The owners of the databases may need to provide answers to certain common aggregated queries using all information available without sharing or transferring all data. It is necessary to minimize the communication costs, improve efficiency, and comply with the legal requirements protecting the privacy of confidential data. In this situation, it is impossible to aggregate the whole database in one location, but effective methods for answers to the aggregated queries with privacy protection are required. To solve this important problem, the present article proposes a Multistage Separate Query Processing (MSQP) protocol employing homomorphic encryption with split keys. We show that our protocol can answer a large class of natural queries of practical significance. The running time of the MSQP protocol is O(d+[Formula presented]), where d is the number of database owners and m is the total number of records in the whole database. In practice, d is small, m can be very large, and so the running time is O(m). This means that the protocol is very efficient for large databases. It dramatically reduces the communication costs of computation and completely eliminates the need for exchange of confidential data. We define a new generalized additive homomorphic property and introduce a Multipart ElGamal Cryptosystem (MEC) with split keys, which enjoys this property. MEC is a novel modification of the ElGamal cryptosystem with split keys. This paper presents the results of extensive experiments evaluating the effectiveness of the MSQP protocol employing MEC and comparing it with MSQP employing the ElGamal cryptosystem, for a collection of publicly available medical datasets. The experiments evaluating our protocol on 11 real-life databases and a synthetic database demonstrate that the MSQP protocol employing MEC is more efficient than other options and can be recommended for practical implementations

Collusion-resistant protocols for private processing of aggregated queries in distributed databases

Private processing of database queries protects the confidentiality of sensitive data when queries are answered. It is important to design collusion-resistant protocols ensuring that privacy remains protected even when a certain number of honest-but-curious participants collude to share their knowledge in order to gain unauthorised access to sensitive information. A novel setting arises when aggregated queries need to be answered for a large distributed database, but legal requirements or commercial interests forbid making access to records in each subdatabase available to other counterparts. For example, a very large number of medical records may be stored in a distributed database, which is a union of several separate databases from different hospitals, or even from different countries. The present article introduces and investigates two protocols for collusion-resistant private processing of aggregated queries in this novel setting: Accelerated Multi-round Iterative Protocol (AMIP) and Restricted Multi-round Iterative Protocol (RMIP). We define a large collection of query functions and show that AMIP and RMIP protocols can answer all queries in this collection. Our experiments demonstrate that the AMIP protocol outperforms all other applicable algorithms, and this achievement is especially significant in terms of the communication complexity