Conducting secure computations to protect against malicious adversaries is an emerging field of research. Current models designed for malicious security typically necessitate the involvement of two or more servers in an honest-majority setting. Among privacy-preserving data mining techniques, significant attention has been focused on the classification problem. Logistic regression emerges as a well-established classification model, renowned for its impressive performance. We introduce a novel matrix encryption method to build a maliciously secure logistic model. Our scheme involves only a single semi-honest server and is resilient to malicious data providers that may deviate arbitrarily from the scheme. Thed-transformation ensures that our scheme achieves indistinguishability (i.e., no adversary can determine, in polynomial time, which of the plaintexts corresponds to a given ciphertext in a chosen-plaintext attack). Malicious activities of data providers can be detected in the verification stage. A lossy compression method is implemented to minimize communication costs while preserving negligible degradation in accuracy. Experiments illustrate that our scheme is highly efficient to analyze large-scale datasets and achieves accuracy similar to non-private models. The proposed scheme outperforms other maliciously secure frameworks in terms of computation and communication costs.

Machine learning and artificial neural networks (ANNs) have been at the forefront of medical research in the last few years. It is well known that ANNs benefit from big data and the collection of the data is often decentralized, meaning that it is stored in different computer systems. There is a practical need to bring the distributed data together with the purpose of training a more accurate ANN. However, the privacy concern prevents medical institutes from sharing patient data freely. Federated learning and multi-party computation have been proposed to address this concern. However, they require the medical data collectors to participate in the deep-learning computations of the data users, which is inconvenient or even infeasible in practice. In this paper, we propose to use matrix masking for privacy protection of patient data. It allows the data collectors to outsource privacy-sensitive medical data to the cloud in a masked form, and allows the data users to outsource deep learning to the cloud as well, where the ANN models can be trained directly from the masked data. Our experimental results on deep-learning models for diagnosis of Alzheimer's disease and Parkinson's disease show that the diagnosis accuracy of the models trained from the masked data is similar to that of the models from the original patient data.

Thanks to the proliferation of Internet access and modern digital and mobile devices, online survey has been flourishing into data collection of marketing, social, financial and medical studies. However, traditional data collection methods in online survey suffer from serious privacy issues. Existing privacy protection techniques are not adequate for online survey for lack of strong privacy. In this paper, we propose a practical strong privacy online survey scheme SPS based on a novel data collection technique called dual matrix masking (DM2), which guarantees the correctness of the tallying results with low computation overhead, and achieves universal verifiability, robustness and strong privacy. We also propose a more robust scheme RSPS, which incorporates multiple distributed survey managers. The RSPS scheme preserves the nice properties of SPS, and further achieves robust strong privacy against joint collusion attack. Through extensive analyses, we demonstrate our proposed schemes can be efficiently applied to online survey with accuracy and strong privacy.

Thanks to the Internet, voters can cast their ballots over the electronic voting (E-voting) systems conveniently and efficiently without going to the polling stations. However, existing E-voting protocols suffer from anonymity issues and/or high deployment overhead. In this paper, we design a practical anonymous E-voting protocol (referred to as MVP) based on a novel data collection technique called dual random matrix masking (DRMM), which guarantees anonymity with low overhead of computation, and achieves the security goals of receipt-freeness, double voting detection, fairness, ballot secrecy, and integrity. Through extensive analyses on correctness, efficiency, and security properties, we demonstrate our proposed MVP protocol can be applied to E-voting in a variety of situations with accuracy and anonymity.

Radio frequency identification (RFID) has been gaining popularity for inventory control, object tracking, and supply-chain management in warehouses, retail stores, hospitals, etc. Periodically and automatically estimating the number of RFID tags deployed in a large area has many important applications in inventory management and theft detection. Prior works focus on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. We observe that for an RFID reader to access tags in a large area, active tags are likely to be used due to their longer operational ranges. These tags are battery-powered and use their own energy for information transmission. However, recharging batteries for tens of thousands of tags is laborious. Hence, conserving energy for active tags becomes critical. Some prior works have studied how to reduce energy expenditure of an RFID reader when it reads tag IDs. We study how to reduce the amount of energy consumed by active tags during the process of estimating the number of tags in a system. We design two energy-efficient probabilistic estimation algorithms that iteratively refine a control parameter to optimize the information carried in transmissions from tags, such that both the number and the size of transmissions are reduced. These algorithms can also take time efficiency into consideration. By tuning a contention probability parameter$\omega$, the new algorithms can make tradeoff between energy cost and estimation time.

RFID has been gaining popularity for inventory control, object tracking, and supply chain management in warehouses, retail stores, hospitals, etc. Periodically and automatically estimating the number of RFID tags deployed in a large area has many important applications in inventory management and theft detection. The prior work focuses on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. We observe that, for a RFID reader to access tags in a large area, active tags are likely to be used. These tags are battery-powered and use their own energy for information transmission. However, recharging batteries for tens of thousands of tags is laborious. Unlike the prior work, this paper studies the RFID estimation problem from the energy angle. Our goal is to reduce the amount of energy that is consumed by the tags during the estimation procedure. We design several energy-efficient probabilistic algorithms that iteratively refine a control parameter to optimize the information carried in the transmissions from the tags, such that both the number and the size of the transmissions are minimized.