Privacy perception and information technology utilization of high school students

Mobile technologies are commonly used and are important by high school students, since teens ages 14 to 17 usethese open platforms to share information, communication and construction of their desired cyber identity.Accompanying technology for related data privacy within implementing educational applications is yet to bedeveloped. This research was designed to investigate the perceptions of data privacy and the protection of per-sonal data of high school students who are surrounded by the Internet, social media and technology. Theperception of high school students' personal data privacy survey was developed and conducted with 1065 highschool students (9th grades). The study presentsfive main themes: (1) ownership and utilization of differenttechnologies and password sharing, (2) Internet utilization and perception of privacy, (3) social media utilizationand perception of personal privacy on social media, (4) knowledge level and perception of personal data con-servation, (5) Information technology utilization. High school students have a personal data privacy algorithm butpersons or institutions outside this algorithm are perceived as a threat to their personal data and are rejected. Thisresearch suggests developing practices and techniques to overcome students' concerns about privacy risks thatresult from the collection and sharing personal data

Privacy-aware publication and utilization of healthcare data

textOpen access to health data can bring enormous social and economical benefits. However, such access can also lead to privacy breaches, which may result in discrimination in insurance and employment markets. Privacy is a subjective and contextual concept, thus it should be interpreted from both systemic and information perspectives to clearly understand potential breaches and consequences. This dissertation investigates three popular use cases of healthcare data: specifically, 1) synthetic data publication, 2) aggregate data utilization, and 3) privacy-aware API implementation. For each case, we develop statistical models that improve the privacy-utility Pareto frontier by leveraging a variety of machine learning techniques such as information theoretic privacy measures, Bayesian graphical models, non-parametric modeling, and low-rank factorization techniques. It shows that much utility can be extracted from health records while maintaining strong privacy guarantees and protection of sensitive health information.Electrical and Computer Engineerin

Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its rise, with limited exceptions, differential privacy has failed to achieve widespread adoption in the enterprise domain. Our study aims to shed light on the fundamental causes underlying this academic-industrial utilization gap through detailed interviews of 24 privacy practitioners across 9 major companies. We analyze the results of our survey to provide key findings and suggestions for companies striving to improve privacy protection in their data workflows and highlight the necessary and missing requirements of existing differential privacy tools, with the goal of guiding researchers working towards the broader adoption of differential privacy. Our findings indicate that analysts suffer from lengthy bureaucratic processes for requesting access to sensitive data, yet once granted, only scarcely-enforced privacy policies stand between rogue practitioners and misuse of private information. We thus argue that differential privacy can significantly improve the processes of requesting and conducting data exploration across silos, and conclude that with a few of the improvements suggested herein, the practical use of differential privacy across the enterprise is within striking distance

Efficient Privacy-Aware Imagery Data Analysis

The widespread use of smartphones and camera-coupled Internet of Thing (IoT) devices triggers an explosive growth of imagery data. To extract and process the rich contents contained in imagery data, various image analysis techniques have been investigated and applied to a spectrum of application scenarios. In recent years, breakthroughs in deep learning have powered a new revolution for image analysis in terms of effectiveness with high resource consumption. Given the fact that most smartphones and IoT devices have limited computational capability and battery life, they are not ready for the processing of computational intensive analytics over imagery data collected by them, especially when deep learning is involved. To resolve the bottleneck of computation, storage, and energy for these resource constrained devices, offloading complex image analysis to public cloud computing platforms has become a promising trend in both academia and industry. However, an outstanding challenge with public cloud is on the protection of sensitive information contained in many imagery data, such as personal identities and financial data. Directly sending imagery data to the public cloud can cause serious privacy concerns and even legal issues. In this dissertation, I propose a comprehensive privacy-preserving imagery data analysis framework which can be integrated in different application scenarios to assist image analysis for resource-constrained devices with efficiency, accuracy, and privacy protection. I first identify security challenges in the utilization of public cloud for image analysis. Then, I design and develop a set of novel solutions to address these challenges. These solutions will be featured by strong privacy guarantee, lightweight computation, low accuracy loss compared with image analysis without privacy protection. To optimize the communication overhead and resource utilization of using cloud computing, I investigate edge computing, which is a promising technique to ameliorate the high communication overhead in cloud-assisted architectures. Furthermore, to boost the performance of my solutions under both cloud and edge deployment, I also provide a set of pluggable enhancement modules to be applied to meet different requirements for various tasks. By exploring the features of edge computing and cloud computing, I flexibly incorporate them as a comprehensive framework to provide privacy-preserving image analysis services

Efficient Multi-User Keyword Search over Encrypted Data in Cloud Computing

As cloud computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. In this paper, we propose a new method to enable effective fuzzy keyword search in a multi-user system over encrypted cloud data while maintaining keyword privacy. In this new system, differential searching privileges are supported, which is achieved with the technique of attribute-based encryption. Edit distance is utilized to quantify keywords similarity and develop fuzzy keyword search technique, which achieve optimized storage and representation overheads. We further propose a symbol-based trie-traverse searching scheme to improve the search efficiency. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search with multiple users

E-DPNCT: An Enhanced Attack Resilient Differential Privacy Model For Smart Grids Using Split Noise Cancellation

High frequency reporting of energy utilization data in smart grids can be used to infer sensitive information regarding the consumer's life style. We propose A Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (DPNCT) to protect the privacy of the smart grid data using noise cancellation protocol with a master smart meter to provide accurate billing and load monitoring. Next, we evaluate the performance of DPNCT under various privacy attacks such as filtering attack, negative noise cancellation attack and collusion attack. The DPNCT model relies on trusted master smart meters and is vulnerable to collusion attack where adversary collude with malicious smart meters in order to get private information of other smart meters. In this paper, we propose an Enhanced DPNCT (E-DPNCT) where we use multiple master smart meters for split noise at each instant in time t for better protection against collusion attack. We did extensive comparison of our E-DPNCT model with state of the art attack resistant privacy preserving models such as EPIC for collision attack and with Barbosa Differentialy Private (BDP) model for filtering attack. We evaluate our E-DPNCT model with real time data which shows significant improvement in privacy attack scenarios without any compute intensive operations.Comment: 10 pages, 12 figues, 4 table

Legal Protection of Personal Data in Artificial Intelligence for Legal Protection Viewed From Legal Certainty Aspect

Protection of personal data is one of the rights possessed by humans, which is one of the privacy rights possessed by a person in maintaining and securing personal data owned by each individual. The development of Artificial Intelligence (AI)-based technology has developed rapidly in the digital world 4.0, where legal protection is needed in personal data protection legal instruments. This research aims to examine the use of AI as a tool in protecting personal data and to examine the urgency of a special regulation in Indonesia in protecting personal data. The research method used in writing this law is normative legal research. In this research, what is meant by juridical research is the 1945 Constitution of the Republic of Indonesia, the Law on Information, and Electronic Transactions Number 11 of 2008, the Regulation of the Minister of Communication and Information Number 20 of 2016, Government Regulation Number 82 of 2012, and UDHR by conducting a study of legal products in the form of laws and regulations. Furthermore, what is meant by normative research is related to the principle of legal certainty, which later can be linked to the urgency of personal data protection regulations for the protection, supervision, and utilization of personal data abuse. Keywords: personal data, artificial intelligence, protection, urgenc

REFORMULASI PENETRATION STRESS TEST SEBAGAI PERLINDUNGAN HUKUM DATA PRIBADI KONSUMEN DI ERA BISNIS DIGITAL

This research focus on consumer personal data protection as a part of invention from privacy right in digital era. The high number of consumer personal data utilization by electronic system operator not accompanied with decent penetration stress test (PST) regulation. The purpose of this research is to give solution to clear personal data protection problem through PST testing method. The method that used by this research is normative research through statutory approach and conceptual approach. The result from this research concludes that there are still many problematic PST regulation. The problem can be seen from personal data protection that shattered in 30 statutory from different sector with no one arrange PST comprehensively. This dispute culminates to emergence dissimilarity definition, overlapping authority between receiver of System Management Security Information certification annual report, and PST operator polemic. As the result, it causes rampant of personal data breach that inflict consumer by matter, along with specific data exploitation that lead to sluggish business and economic country. Therefore, PST testing reformulation is needed as prevention step to protect consumer personal data in digital business era