A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents

The crosswise model is an increasingly popular survey technique to elicit candid answers from respondents on sensitive questions. Recent studies, however, point out that in the presence of inattentive respondents, the conventional estimator of the prevalence of a sensitive attribute is biased toward 0.5. To remedy this problem, we propose a simple design-based bias correction using an anchor question that has a sensitive item with known prevalence. We demonstrate that we can easily estimate and correct for the bias arising from inattentive respondents without measuring individual-level attentiveness. We also offer several useful extensions of our estimator, including a sensitivity analysis for the conventional estimator, a strategy for weighting, a framework for multivariate regressions in which a latent sensitive trait is used as an outcome or a predictor, and tools for power analysis and parameter selection. Our method can be easily implemented through our open-source software cWise.

Bayesian Estimation ofWarner’s Randomized Response Technique with Transmuted Kumaraswamy Prior

In recent time, the Bayesian approach to randomized response technique has been used for estimating the population proportion especially of respondents possessing sensitive attributes such as induced abortion, tax evasion and shoplifting. This is done by combining suitable prior information about an unknown parameter of the population with the sample information for the estimation of the unknown parameter. In this study, possibility of using a transmuted Kumaraswamy prior is raised, yielding a new Bayes estimator for estimating population proportion of sensitive attribute for Warner’s randomized response technique. Consequently, the proposed Bayes estimator with transmuted Kumaraswamy prior is compared with existing Bayes estimators developed with a simple beta and Kumaraswamy priors in terms of their mean square error. The proposed estimator competes well with the existing estimators for some values of population proportion. The performances of Bayes estimators were also compared using some benchmark data.

SUSTAINABLE MANAGEMENT POLICY STRATEGY OF COASTAL TOURISM IN PANGANDARAN DISTRICT, INDONESIA

One of the crucial coastal tourism is a region of Pangandaran District coastal tourism. The area represented a coastal region that has various exploiting characteristics and interconnected one another. The primary purpose of this research is to analyze the sustainable management policy strategy of coastal tourism. To reach the especial target, hence there are some activities which require to be conducted as a particular target that is (1) identifying determinant in the future, (2) determining strategic target and importance of the main stakeholder ; and (3) defining and describe of evolution possibility of future. The prospective analysis was conducted to yield a sustainable regional development scenario of coastal tourism in Pangandaran District, with determining key factors that affect system performance. From various possibilities that could happen, is formulated three regional development scenario of Pangandaran District coastal tourism to come, that are : 1) Conservative - Pessimistic by conducting to repair of main key factor only, 2) Moderate - Optimistic by conducting repair about 50 % of the primary key attribute (factor), 3) Progressive - Optimistic by conducting repair to entire key attribute (factor). To increase sustainable status forwards (long-period), a scenario that must be conducted to increase the regional sustainable development status of the coastal of Pangandaran District is Progressive – Optimistic by conducting repair by totally to all sensitive attribute so that all dimension become sustainable for coastal region development.

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

Does Conjoint Analysis Mitigate Social Desirability Bias?

How can we elicit honest responses in surveys? Conjoint analysis has become a popular tool to address social desirability bias (SDB), or systematic survey misreporting on sensitive topics. However, there has been no direct evidence showing its suitability for this purpose. We propose a novel experimental design to identify conjoint analysis’s ability to mitigate SDB. Specifically, we compare a standard, fully randomized conjoint design against a partially randomized design where only the sensitive attribute is varied between the two profiles in each task. We also include a control condition to remove confounding due to the increased attention to the varying attribute under the partially randomized design. We implement this empirical strategy in two studies on attitudes about environmental conservation and preferences about congressional candidates. In both studies, our estimates indicate that the fully randomized conjoint design could reduce SDB for the average marginal component effect (AMCE) of the sensitive attribute by about two-thirds of the AMCE itself. Although encouraging, we caution that our results are exploratory and exhibit some sensitivity to alternative model specifications, suggesting the need for additional confirmatory evidence based on the proposed design.

kNN-based Gas-bearing Prediction Using Local Waveform Similarity Gas-indication Attribute - An Application to a Tight Sandstone Reservoir

Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k Nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.

Interplay between upsampling and regularization for provider fairness in recommender systems

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where (i) the same provider is associated with multiple items of a list suggested to a user, (ii) an item is created by more than one provider jointly, and (iii) predicted user–item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user–item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility.

Stack and Deal: An Efficient Algorithm for Privacy Preserving Data Publishing

Although k-Anonymity is a good way to publish microdata for research purposes, it still suffers from various attacks. Hence, many refinements of k-Anonymity have been proposed such as ldiversity and t-Closeness, with t-Closeness being one of the strictest privacy models. Satisfying t-Closeness for a lower value of t may yield equivalence classes with high number of records which results in a greater information loss. For a higher value of t, equivalence classes are still prone to homogeneity, skewness, and similarity attacks. This is because equivalence classes can be formed with fewer distinct sensitive attribute values and still satisfy the constraint t. In this paper, we introduce a new algorithm that overcomes the limitations of k-Anonymity and lDiversity and yields equivalence classes of size k with greater diversity and frequency of a SA value in all the equivalence classes differ by at-most one.

f-Slip: An Efficient Privacy-Preserving Data Publishing Framework for 1: M Microdata with Multiple Sensitive Attributes.

Privacy of the individuals plays a vital role when a dataset is disclosed in public. Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes of analysis and research. The data to be published contain several sensitive attributes such as diseases, salary, symptoms, etc. Earlier, researchers have dealt with datasets considering it would contain only one record for an individual [1:1 dataset], which is uncompromising in various applications. Later, many researchers concentrate on the dataset, where an individual has multiple records [1:M dataset]. In the paper, a model f-slip was proposed that can address the various attacks such as Background Knowledge (bk) attack, Multiple Sensitive attribute correlation attack (MSAcorr), Quasi-identifier correlation attack(QIcorr), Non-membership correlation attack(NMcorr) and Membership correlation attack(Mcorr) in 1:M dataset and the solutions for the attacks. In f -slip, the anatomization was performed to divide the table into two subtables consisting of i) quasi-identifier and ii) sensitive attributes. The correlation of sensitive attributes is computed to anonymize the sensitive attributes without breaking the linking relationship. Further, the quasi-identifier table was divided and k-anonymity was implemented on it. An efficient anonymization technique, frequency-slicing (f-slicing), was also developed to anonymize the sensitive attributes. The f -slip model is consistent as the number of records increases. Extensive experiments were performed on a real-world dataset Informs and proved that the f -slip model outstrips the state-of-the-art techniques in terms of utility loss, efficiency and also acquires an optimal balance between privacy and utility.

An Improved Two-stage Randomized Response Model for Estimating the Proportion of Sensitive Attribute

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage RRT model to estimate the prevalence of sensitive attribute ([Formula: see text]). A simulation study shows that the empirical mean and variance of proposed estimator are close to corresponding theoretical values. The utility of proposed two-stage RRT model under stratification is also explored. An efficiency comparison between proposed two-stage RRT model and some existing RRT models is carried out numerically under simple and stratified random sampling.