28,644 research outputs found
Privacy preserving algorithms for newly emergent computing environments
Privacy preserving data usage ensures appropriate usage of data without compromising sensitive information. Data privacy is a primary requirement since customers' data is an asset to any organization and it contains customers' private information. Data seclusion cannot be a solution to keep data private. Data sharing as well as keeping data private is important for different purposes, e.g., company welfare, research, business etc. A broad range of industries where data privacy is mandatory includes healthcare, aviation industry, education system, federal law enforcement, etc.In this thesis dissertation we focus on data privacy schemes in emerging fields of computer science, namely, health informatics, data mining, distributed cloud, biometrics, and mobile payments. Linking and mining medical records across different medical service providers are important to the enhancement of health care quality. Under HIPAA regulation keeping medical records private is important. In real-world health care databases, records may well contain errors. Linking the error-prone data and preserving data privacy at the same time is very difficult. We introduce a privacy preserving Error-Tolerant Linking Algorithm to enable medical records linkage for error-prone medical records. Mining frequent sequential patterns such as, patient path, treatment pattern, etc., across multiple medical sites helps to improve health care quality and research. We propose a privacy preserving sequential pattern mining scheme across multiple medical sites. In a distributed cloud environment resources are provided by users who are geographically distributed over a large area. Since resources are provided by regular users, data privacy and security are main concerns. We propose a privacy preserving data storage mechanism among different users in a distributed cloud. Managing secret key for encryption is difficult in a distributed cloud. To protect secret key in a distributed cloud we propose a multilevel threshold secret sharing mechanism. Biometric authentication ensures user identity by means of user's biometric traits. Any individual's biometrics should be protected since biometrics are unique and can be stolen or misused by an adversary. We present a secure and privacy preserving biometric authentication scheme using watermarking technique. Mobile payments have become popular with the extensive use of mobile devices. Mobile applications for payments needs to be very secure to perform transactions and at the same time needs to be efficient. We design and develop a mobile application for secure mobile payments. To secure mobile payments we focus on user's biometric authentication as well as secure bank transaction. We propose a novel privacy preserving biometric authentication algorithm for secure mobile payments
Privacy-preserving blockchain based IoT ecosystem using attribute-based encryption
© 2017 IEEE. The Internet of Things (IoT) has penetrated deeply into our lives and the number of IoT devices per person is expected to increase substantially over the next few years. Due to the characteristics of IoT devices (i.e., low power and low battery), usage of these devices in critical applications requires sophisticated security measures. Researchers from academia and industry now increasingly exploit the concept of blockchains to achieve security in IoT applications. The basic idea of the blockchain is that the data generated by users or devices in the past are verified for correctness and cannot be tampered once it is updated on the blockchain. Even though the blockchain supports integrity and non-repudiation to some extent, confidentiality and privacy of the data or the devices are not preserved. The content of the data can be seen by anyone in the network for verification and mining purposes. In order to address these privacy issues, we propose a new privacy-preserving blockchain architecture for IoT applications based on attribute-based encryption (ABE) techniques. Security, privacy, and numerical analyses are presented to validate the proposed model
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information
and Knowledge Management (CIKM 2018
Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing
In this paper, we propose a privacy preserving distributed
clustering protocol for horizontally partitioned data based on a very efficient
homomorphic additive secret sharing scheme. The model we use
for the protocol is novel in the sense that it utilizes two non-colluding
third parties. We provide a brief security analysis of our protocol from
information theoretic point of view, which is a stronger security model.
We show communication and computation complexity analysis of our
protocol along with another protocol previously proposed for the same
problem. We also include experimental results for computation and communication
overhead of these two protocols. Our protocol not only outperforms
the others in execution time and communication overhead on
data holders, but also uses a more efficient model for many data mining
applications
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