Privacy Preserving Data Classification Research Articles

Blockchain-Based Auditable Privacy-Preserving Data Classification for Internet of Things

Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain users’ sensitive information. When guaranteeing the utility of data, we need to consider privacy of users’ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPDC</i> ) scheme for IoT. We put forth a new controllably linkable group signature ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CL-GS</i> ) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPDC</i> .

Firefly Optimization Based Noise Additive Privacy-Preserving Data Classification Technique to Predict Chronic Kidney Disease

With the continuous advancements in Information and Communication Technology, healthcare data is stored in the electronic forms and accessed remotely according to the requirements. However, there is a negative impact like unauthorized access, misuse, stealing of the data, which violates the privacy concern of patients. Sensitive information, if not protected, can become the basis for linkage attacks. Paper proposes an improved Privacy-Preserving Data Classification System for Chronic Kidney Disease dataset. Focus of the work is to predict the disease of patients’ while preventing the privacy breach of their sensitive information. To accomplish this goal, a metaheuristic Firefly Optimization Algorithm (FOA) is deployed for random noise generation (instead of fixed noise) and this noise is added to the least significant bits of sensitive data. Then, random forest classifier is applied on both original and perturbed dataset to predict the disease. Even after perturbation, technique preserves required significance of prediction results by maintaining the balance between utility and security of data. In order to validate the results, proposed method is compared with the existing technology on the basis of various evaluation parameters. Results show that proposed technique is suitable for healthcare applications where both privacy protection and accurate prediction are necessary conditions.

Blockchain-based verifiable privacy-preserving data classification protocol for medical data

Massive IoT devices are used for data collection with the fast development of the Internet-of-things (IoT). For example, wearable devices are used to collect users’ health data and conduct health monitoring. However, many private information are involved in medical data. Privacy-preserving data classification scheme (PPDC) provided an effective approach to balance the utility and the privacy of data. In the PPDC schemes, a fully trusted auditor is employed to validate the result of data classification. To reduce trust on the auditor, we provide a simplified version of the PPDC scheme. We propose blockchain-based verifiable privacy-preserving data classification protocol (VeriDC) for medical data. It makes the data center check the classification result without involving an auditor. We provide the system model of the verifiable privacy-preserving data classification protocol in blockchain setting and formalize its security model. By using verifiable oblivious pseudorandom function(verifiable OPRF), we can generate a verifiable proof and post it on blockchain to guarantee the transparency of data classification. We present a concrete construction and prove its security. Finally, we compare VeriDC with the related schemes to evaluate the effectiveness of VeriDC.

Toward Transparent and Accountable Privacy-Preserving Data Classification

Machine learning provides an effective approach to execute big data analysis. As a branch of machine learning, classification has been widely adopted in data processing. However, the sensitivity of data raises the concern of data privacy. How to balance data utility and data privacy is a challenging issue. Privacy-preserving data classification, which supports flexible and privacy-friendly access to datasets and data classification, enables users' data to be collected in an authenticated manner. However, the priva-cy-preserving data classification approach has a limitation in that the correctness of data classification cannot be guaranteed. As a consequence, it is possible for a malicious classifier to manipulate the classification result. To solve these problems, in this article, we propose a transparent and accountable privacy-preserving data classification framework, which involves a tracer to assert the behavior of the classifier and maintains the utility and privacy of data. Specifically, we take advantage of cryptography techniques to balance data privacy and data utility, and use blockchain to achieve transparency and accountability for the behavior of the classifier. To illustrate the practicability of this framework, we implement concrete cryptographic algorithms and develop a prototype system to evaluate and test its performance.

Lightweight privacy-Preserving data classification

Internal attacks are of a huge concern, because they are usually delicately masqueraded under harmless-looking activities, which are very difficult to detect. Machine learning techniques have been successfully applied to identify insider threats. However, they may violate user privacy since they can legally access user’s sensitive information. To preserve user privacy, encryption algorithms have been lately exploited as a powerful tool, to hide private data in a multiple-party collaboration. A combination of encryption and data mining techniques raises high computational complexity. Hence, in order to improve the system’s performance while securing both user’s private data and the classifier, we propose a new secure data analysis protocol, namely SmartClass, by adopting the garbled circuit technique to speed-up the system performance. We developed an efficient encryption step that exploits the additive homomorphism and best properties of the binary Elliptic Curve Cryptography (ECC) algorithm, while keeping the protocol highly secure. We implemented the proposed system and study its effectiveness. Experimental results show the proposed approach is very promising.

Efficient privacy preservation of big data for accurate data mining

Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potentially sensitive data may become public if the collected data are not appropriately sanitized before being released for investigation. Although there are more than a few privacy-preserving methods available, they are not efficient, scalable, or have problems with data utility, or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via optimal geometric transformations. PABIDOT was tested for efficiency, scalability, attack resistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance, and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.

SW-SDF based privacy preserving data classification

The core objective of privacy preserving data mining is to preserve the confidentiality of individual even after mining. The basic advantage of personalized privacy preservation is that the information loss is very less as compared with other privacy preservation algorithms. These algorithms how ever have not been designed for specific mining algorithms. SW-SDF personalized privacy preservation uses two flags SW and SDF. SW is used for assigning a weight for the sensitive attribute and SDF for sensitive disclosure which is accepted from individual. In this paper we have designed an algorithm which uses SW-SDF personal privacy preservation for data classification. This method ensures privacy and classification of data.