Achievable Sum Rates of Half- and Full-Duplex Bidirectional OFDM Communication Links

While full-duplex (FD) transmission has the potential to double the system capacity, its substantial benefit can be offset by the self-interference (SI) and non-ideality of practical transceivers. In this paper, we investigate the achievable sum rates (ASRs) of half-duplex (HD) and FD transmissions with orthogonal frequency division multiplexing (OFDM), where the non-ideality is taken into consideration. Four transmission strategies are considered, namely HD with uniform power allocation (UPA), HD with non-UPA (NUPA), FD with UPA, and FD with NUPA. For each of the four transmission strategies, an optimization problem is formulated to maximize its ASR, and a (suboptimal/optimal) solution with low complexity is accordingly derived. Performance evaluations and comparisons are conducted for three typical channels, namely symmetric frequency-flat/selective and asymmetric frequency-selective channels. Results show that the proposed solutions for both HD and FD transmissions can achieve near optimal performances. For FD transmissions, the optimal solution can be obtained under typical conditions. In addition, several observations are made on the ASR performances of HD and FD transmissions.Comment: To appear in IEEE TVT. This paper solves the problem of sum achievable rate optimization of bidirectional FD OFDM link, where joint time and power allocation is involve

Protecting Privacy Against Regression Attacks in Predictive Data Mining

Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-mining technique, can be used to effectively reveal individuals\u27 sensitive data. This problem, which we call a regression attack, has been overlooked in the literature. Existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach adopts a novel measure which considers the tradeoff between disclosure risk and data utility in a regression tree pruning process. We also propose a dynamic value-concatenation method, which overcomes the limitation of requiring a user-defined generalization hierarchy in traditional k-anonymity approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted to demonstrate the effectiveness of the proposed approach

DATA CLUSTERING AND MICRO-PERTURBATION FOR PRIVACY-PRESERVING DATA SHARING AND ANALYSIS

Clustering-based data masking approaches are widely used for privacy-preserving data sharing and data mining. Existing approaches, however, cannot cope with the situation where confidential attributes are categorical. For numeric data, these approaches are also unable to preserve important statistical properties such as variance and covariance of the data. We propose a new approach that handles these problems effectively. The proposed approach adopts a minimum spanning tree technique for clustering data and a micro-perturbation method for masking data. Our approach is novel in that it (i) incorporates an entropy-based measure, which represents the disclosure risk of the categorical confidential attribute, into the traditional distance measure used for clustering in an innovative way; and (ii) introduces the notion of cluster-level microperturbation (as opposed to conventional micro-aggregation) for masking data, to preserve the statistical properties of the data. We provide both analytical and empirical justification for the proposed methodology

A Data Perturbation Approach to Privacy Protection in Data Mining

Advances in data mining techniques have raised growing concerns about privacy of personal information. Organizations that use their customers’ records in data mining activities are forced to take actions to protect the privacy of the individuals involved. A common practice for many organizations today is to remove the identity-reated attributes from customer records before releasing them to data miners or analysts. In this study, we investigate the effect of this practice and demonstrate that a majority of the records in a dataset can be uniquely identified even after identity related attributes are removed. We propose a data perturbation method that can be used by organizations to prevent such unique identification of individual records, while providing the data to analysts for data mining. The proposed method attempts to preserve the statistical properties of the data based on privacy protection parameters specified by the organization. We show that the problem can be solved in two phases, with a linear programming formulation in phase one (to preserve the marginal distribution), followed by a simple Bayes-based swapping procedure in phase two (to preserve the joint distribution). The proposed method is compared with a random perturbation method in classification performance on two real-world datasets. The results of the experiments indicate that it significantly outperforms the random method

From Mistakes to Insights: Counterfactual Explanations for Incorrect Machine Learning Predictions

Machine learning (ML) has revolutionized various industries with powerful predictive capabilities. However, the lack of interpretability in these black box models poses challenges in high-stakes domains like finance, healthcare, and criminal justice. Interpretable machine learning (IML) or explainable AI (XAI) aims to address these challenges by developing methods that provide meaningful explanations for human understanding (Molnar, 2023). By enhancing interpretability, we can establish trust, transparency, and accountability in AI systems, ensuring fairness and reliability in their outputs. Counterfactual explanations have gained popularity as an XAI/IML method (Verma, Boonsanong, Hoang, Hines, Dickerson and Shah, 2022). Unlike traditional methods, counterfactual explanations don\u27t directly explain the why behind a decision. Instead, they present alternative scenarios, or counterfactuals, illustrating how changes in inputs or features could lead to different outcomes. For instance, if an ML model predicts a loan default, a counterfactual explanation can advise the applicant on the factors that could secure loan approval. Counterfactual explanations are easy to understand, persuasive, and provide actionable insights (Fernández-Loría, Provost, and Han, 2022), leading to increased research attention (Guidotti, 2022). Fidelity is a crucial criterion for evaluating XAI/IML methods, measuring their ability to approximate black box model predictions accurately. However, existing approaches solely prioritize fidelity and overlook errors. When a black box model misclassifies an instance, interpretable methods, based on fidelity, mistakenly treat the misclassified result as correct and attempt to explain the incorrect outcome. This misinterpretation has significant implications for subsequent actions, and no existing studies have addressed this issue. In this study, we address the problem of rectifying and explaining incorrect predictions made by AI and ML models. Our focus is on classification problems with two categorical outcomes: beneficial and adverse. Two types of errors exist: misclassifying beneficial as adverse (b2a) and misclassifying adverse as beneficial (a2b). We distinguish between two types of errors, and our research questions are (1) how to explain misclassifications for individuals when a beneficial class is classified as adverse and (2) for organizations when an adverse class is classified as beneficial. We propose a novel and practical method for providing explanations in misclassified cases using a counterfactual explanation approach applicable to any classification model. Our method involves using a black box model to classify instances and fitting a decision tree, called an explanation tree, based on the black box model\u27s classification results. This tree helps identify the best counterfactual examples for explanations, tailored to individual customers and organizational decision-makers and analysts. This work contributes to machine learning and business analytics research in several ways: (1) We investigate the unexplored problem of rectifying and explaining misclassified outcomes made by ML models. (2) We propose a practical method for providing counterfactual explanations in correctly and incorrectly classified cases. (3) We validate our method through an empirical evaluation study using real-world data

Protecting Privacy When Sharing and Releasing Data with Multiple Records per Person

This study concerns the risks of privacy disclosure when sharing and releasing a dataset in which each individual may be associated with multiple records. Existing data privacy approaches and policies typically assume that each individual in a shared dataset corresponds to a single record, leading to an underestimation of the disclosure risks in multiple records per person scenarios. We propose two novel measures of privacy disclosure to arrive at a more appropriate assessment of disclosure risks. The first measure assesses individual-record disclosure risk based upon the frequency distribution of individuals’ occurrences. The second measure assesses sensitive-attribute disclosure risk based upon the number of individuals affiliated with a sensitive value. We show that the two proposed disclosure measures generalize the well-known k-anonymity and l-diversity measures, respectively, and work for scenarios with either a single record or multiple records per person. We have developed an efficient computational procedure that integrates the two proposed measures and a data quality measure to anonymize the data with multiple records per person when sharing and releasing the data for research and analytics. The results of the experimental evaluation using real-world data demonstrate the advantage of the proposed approach over existing techniques for protecting privacy while preserving data quality