Non-sensitive Variable Research Articles

An efficient estimator of population variance of a sensitive variable with a new randomized response technique

In sampling theory, a majority of the available estimators of population variance are designed for use with non-sensitive variables only. Such estimators cannot perform efficiently when the variable of interest is of sensitive nature, such as use of drugs, illegal income, abortion, cheating in examination, the amount of income tax payable, and the violation of rules by employees, etc. In the current literature, the shortage of research studies on variance estimators of a sensitive variable has created a big research gap and a room for improvement in the efficiency of such estimators. In this paper, a new randomized scrambling technique is proposed, along with a new estimator of population variance. The new estimator achieves improvement in efficiency over the available variance estimators. The proposed estimator is designed for use with simple random sampling and uses the information on an auxiliary variable. The improvement in efficiency is shown for different choices of constants. Besides efficiency, improvement in the unified measure of estimator quality is also achieved with the proposed estimator under the new randomized response model.

An introduction to sensible constraints for the imputation of missing values

Motivated by Haq et al. (2017), Mohamed et al. (2018) and some recent developments for the estimation of finite population mean, we propose a new design based imputation procedure for the imputation of missing values by applying some sensible constraints on the study and the auxiliary variables, respectively. The proposed design is considered under two assumptions: (i) the study variable is a non-sensitive variable that the measurements on the study variable do not create any embarrassment during personal interview and (ii) the study variable is a sensitive variable where the measurement errors are introduced due to some untruthful responses. These measurement errors are minimized up to some extend by using the scrambling response models. The proposed imputed values are obtained by the dual use of the auxiliary information leading to a consistent estimator that seems like the linear regression method of imputation. Expressions for the mean square error (MSE) are obtained up to the first order approximation. Finally, simulation studies are carried out in favor of the proposed estimators.

FloVasion: Towards Detection of non-sensitive Variable Based Evasive Information-Flow in Android Apps

ABSTRACT Smartphones are enriched by applications (apps) available through the mobile ecosystem. Various studies have reported that apps leaking sensitive user and device information are the primary target of cyber criminals. Existing program analysis tools can detect such information leakage flows. Reverse engineering tools are deployed to determine app information-flow via control and data-flow analysis. Malware authors employ information-flow based evasion techniques while leaking privacy sensitive data. In this paper, we discuss five novel app attacks that evade information flow analysis and leak sensitive device and user information (e.g. IMEI, SIM details, Location details, and user contacts). These attacks circumvent state-of-the-art analysis tools. We show that sensitive information can be leaked via non-sensitive variables, or by performing runtime inspection of classes and fields. We analyzed the proposed novel attack apps against some of the most promising state-of-the-art static analysis tools such as FlowDroid, DroidSafe, and dynamic analysis tools such as TaintDroid. Furthermore, we evaluated Play-Protect i.e. default on-device anti-malware, AVL Antivirus, and some other top commercial products against proposed novel app attacks. We demonstrate that existing tools are vulnerable to proposed attacks. Finally, this paper proposes AspectJ based runtime monitor as a possible solution that can be incorporated in the state-of-the-art app analysis techniques to detect information flow misuse.

A modified regression-cum-ratio estimator of population mean of a sensitive variable in the presence of non-response in simple random sampling

A generalized class of estimators has been proposed for finite population mean of a sensitive study variable in the presence of non-response under a Simple Random Sampling Without Replacement design (SRSWOR) using the Randomized Response Technique (RRT) in two phase sampling . Auxiliary information from a positively correlated non-sensitive variable is used. The expressions for MSE of the proposed estimator are derived. The performance of the proposed estimator is evaluated both theoretically and empirically in comparison to some of the existing estimators.

Multi-Objective Coordinated Control of Reactive Compensation Devices Among Multiple Substations

The multi-objective reactive power coordination control model was presented in this paper to overcome the drawback that reactive power compensation devices lack in coordination and have high active power loss. In this model, the minimum bus voltage deviation and total power loss of reactive power compensation devices are taken as objective functions, by bringing the lower voltage reactors and capacitors into the static var compensator control system and considering the reactive power compensation devices interaction between different transformer substations. Due to the different sensitivity of control variables in control model and lack of the local search ability, control variables are divided into sensitive variables and non-sensitive variables. Then, the improved non-dominated sorting genetic algorithm II (NSGA-II) with secondary search ability is used to search the Pareto optimal solution set. Two substations in large power grid with strong voltage coupling are coordinated in different load level. The results obtained by proposed NSGA-II algorithm can provide a variety of optimal control strategies. Compared to the conventional NSGA-II algorithm and normal boundary intersection method, the improved NSGA-II algorithm has better convergence curve and distribution of the Pareto solution sets.

Batch process monitoring based on self-adaptive subspace support vector data description

Inherent time-varying dynamics, which is a general characteristic of batch processing, causes two problems in data-driven batch process monitoring methods: (1) changes in data trajectory and (2) changes in correlation between variables along time. These problems can be solved by employing monitoring methods based on moving time window technology. However, correlation behaviors between variables in dynamic batch processing are complex. As a consequence, traditional monitoring methods may fail to detect faults. Complex correlation behaviors of batch processing can be learned by placing variables with similar variation information in the same subspace and faults may be detected. In this study, a self-adaptive subspace support vector data description (SASSVDD) is proposed. Two-time unfolding three-dimensional data technology and moving time window technology are used to obtain modeling data. An online subspace is then constructed by using sensitive variables, which may highly yield variation information, and non-sensitive variables, which likely contain variation information and exhibit a higher correlation with sensitive variables. Support vector data description is applied as the subspace monitoring method. The availability of SASSVDD is verified through the fed-batch penicillin fermentation.

Research Note—Applying the Randomized Response Technique to Elicit Truthful Responses to Sensitive Questions in IS Research: The Case of Software Piracy Behavior

Research on software piracy often relies on self-reports by individual users and thus suffers from possible response distortion attributable to a variety of human motivations. Conclusions drawn directly from distorted self-reports may misguide managerial and policy decisions. The randomized response technique (RRT) was proposed as a remedy to response distortion. In this paper, a model based on RRT was used to illustrate how truthful responses to sensitive questions can be empirically estimated. The model was tested in two empirical studies on software piracy. Consistent with our expectations, respondents responding to RRT were more willing to disclose sensitive information about their attitudes, intentions, and behaviors on software piracy. Nontrivial distortions were demonstrated in causal relationships involving sensitive and nonsensitive variables. The study extends RRT to multivariate analysis and illustrates the feasibility and usefulness of the method in studying sensitive behavioral issues in the information systems (IS) domain.

A Unified Approach for Analysis of Randomized Response Surveys

In this paper, we propose a class of estimators for the population mean of a sensitive variable, taking account into a generic randomization scheme, under the simple random sampling with replacement (SRSWR), when the mean of a supplementary non-sensitive variable is known. The minimum attainable variance bound of the class is obtained and the best estimator is also defined. We prove that the best estimator acts as a regression estimator which is at least as efficient as the corresponding estimator without the auxiliary variable. A new measure of privacy protection is built, and some models can be compared from the perspective of efficiency and privacy protection.

Dynamic Numerical Computation and Optimum Analysis of Robot Chassis Using Finite Element Method

As the research object, the mobile robot chassis is designed. Firstly, the mobile robot chassis intensity design is processed in accordance with the chassis external load. According to the design results, the structure model is constructed in ANSYS, the strength and stiffness is checked and its structural dynamics characteristic is computed. Then, based on the numerical value analysis, the non-sensitive variable of the bodywork is analyzed and the optimization model of the body structure is established. The result of the Numerical Optimization proved that, the inherent structure frequency characteristics did not change significantly in the case of the body weight components reduced. The mobile robot platform is set up in the laboratory, the experimental results showed that the robot chassis design is reasonable, in line with the requirements of mobile robots.

Estimating a sensitive proportion through randomized response procedures based on auxiliary information

Randomized response techniques are widely employed in surveys dealing with sensitive questions to ensure interviewee anonymity and reduce nonrespondents rates and biased responses. Since Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering work, many ingenious devices have been suggested to increase respondent’s privacy protection and to better estimate the proportion of people, πA, bearing a sensitive attribute. In spite of the massive use of auxiliary information in the estimation of non-sensitive parameters, very few attempts have been made to improve randomization strategy performance when auxiliary variables are available. Moving from Zaizai’s (Model Assist Stat Appl 1:125–130, 2006) recent work, in this paper we provide a class of estimators for πA, for a generic randomization scheme, when the mean of a supplementary non-sensitive variable is known. The minimum attainable variance bound of the class is obtained and the best estimator is also identified. We prove that the best estimator acts as a regression-type estimator which is at least as efficient as the corresponding estimator evaluated without allowing for the auxiliary variable. The general results are then applied to Warner and Simmons’ model.