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A systematic review of technologies and solutions to improve security and privacy protection of citizens in the smart city
بررسی سیستماتیک فناوری ها و راه حل ها برای بهبود امنیت و حفاظت از حریم خصوصی شهروندان در شهر هوشمند-2022 The development of smart cities through digital communications has improved citizens’ quality of
life and well-being. In these cities, IoT technology generates vast amounts of data at any given
time, which is analyzed to provide services to citizens. In the proper implementation of these
cities, a critical challenge is the violation of citizens’ privacy and security, which leads to a lack of
trust and pessimism toward the services of the smart city. To ensure citizens’ participation, smart
city developers should adequately protect their security and privacy from gaining their trust. If
citizens don’t want to participate, the main benefits of a smart city will be lost. This article
presents a comprehensive review of smart city security issues and privacy. It provides a basis for
categorizing current and future developments in this area and developing a thematic classifica-
tion to highlight the requirements and security strategies for designing a smart and safe city. The
paper identifies current security and privacy solutions and describes open research challenges and
issues. An output of this study is a systematic map of literature on the subject that identifies
critical concepts, evidence, challenges, solutions, and gaps. It summarizes the findings into a body
of evidence that has previously been heterogeneous and complex. keywords: مرور سیستماتیک توصیفی | شهر هوشمند | اینترنت اشیا | حریم خصوصی | امنیت | Descriptive systematic review | Smart city | IoT | Privacy | Security |
مقاله انگلیسی |
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A survey on privacy protection in blockchain system
مروری بر حفاظت از حریم خصوصی در سیستم بلاکچین-2019 Blockchain, as a decentralized and distributed public ledger technology in peer-to-peer network, has received
considerable attention recently. It applies a linked block structure to verify and store data, and applies the
trusted consensus mechanism to synchronize changes in data, which makes it possible to create a tamper-proof
digital platform for storing and sharing data. It is believed that blockchain can be utilized in diverse Internet
interactive systems (e.g., Internet of Things, supply chain systems, identity management, and so on). However,
there are some privacy challenges that may hinder the applications of blockchain. The goal of this survey is
to provide some insights into the privacy issues associated with blockchain. We analyze the privacy threats
in blockchain and discuss existing cryptographic defense mechanisms, i.e., anonymity and transaction privacy
preservation. Furthermore, we summarize some typical implementations of privacy preservation mechanisms in
blockchain and explore future research challenges that still need to be addressed in order to preserve privacy
when blockchain is used. Keywords: Privacy | Anonymity | Blockchain | Cryptography | Cryptocurrency |
مقاله انگلیسی |
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Scheduling workflows with privacy protection constraints for big data applications on cloud
جریان های برنامه ریزی شده با محدودیت های حفاظت از حریم خصوصی برای برنامه های داده بزرگ در ابر-2018 Nowadays, business or scientific processes with massive big data in Cyber-Physical-Social environments
are springing up in cloud. Cloud customers’ private information stored in cloud may be easily exposed and
lead to serious privacy leakage issues in Cyber-Physical-Social environments. To avoid such issues, cloud
customers’ privacy or sensitive data may be restricted to being processed by some specific trusted cloud
data centers. Therefore, a new problem is how to schedule workflow with such data privacy protection
constraints, while minimizing both execution time and monetary cost for big data applications on cloud.
In this paper, we model such problem as a multi-objective optimization problem and propose a Multi
Objective Privacy-Aware workflow scheduling algorithm, named MOPA. It can provide cloud customers
with a set of Pareto tradeoff solutions. The problem-specific encoding and population initialization are
proposed in this algorithm. The experimental results show that our algorithm can obtain higher quality
solutions when compared with other ones.
Keywords: Privacy protection ، Workflow scheduling ، Cloud ، Big data ، Multi-objective optimization |
مقاله انگلیسی |
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Data sanitization in association rule mining: An analytical review
پاکسازی داده در کاوش قانون انجمنی : یک مرور تحلیلی-2018 Association rule hiding is the process of transforming a transaction database into a sanitized version to
protect sensitive knowledge and patterns. The challenge is to minimize the side effects on the sanitized
database. Many different sanitization algorithms have been proposed to reach this purpose. This article
presents a structured analysis and categorization of the existing challenges and directions for state-of-the
art sanitization algorithms, with highlighting about their characteristics. Fifty-four scientific algorithms,
primarily spanning the period 2001–2017, were analyzed and investigated in terms of four aspects in
cluding hiding strategy, sanitization technique, sanitization approach, and selection method. In terms of
results and findings, this review showed that (i) in comparison to other aspects of sanitization algorithms,
the transaction and item selection methods more significantly influence the optimality of hiding process,
(ii) blocking technique increases the disclosure risk while distortion technique is better in knowledge
protection field, and transaction deletion/insertion technique is a new direction, (iii) heuristic-based al
gorithms have attracted more attention than other algorithms, especially in the context of hiding the
association rules, (iv) a new trend is to use evolutionary paradigm for knowledge hiding that is often
integrated with the transaction deletion/insertion technique, and (V) hiding the association rules intro
duces more challenges than hiding the frequent itemsets in terms of the determination of strategy and
formulation of the selection method. This study aims to help researchers and database administrators
find recent developments in association rule hiding.
Keywords: Privacy preserving in data mining ، Association rule mining ، Association rule hiding ، Data sanitization |
مقاله انگلیسی |
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Protecting the privacy of humans in video sequences using a computer vision-based de-identification pipeline
حفاظت از حریم خصوصی افراد در توالی های ویدئویی با استفاده از شناسایی مبتنی بر دید کامپیوتری لوله ای-2017 Article history:Received 12 October 2016Revised 5 May 2017Accepted 27 May 2017Keywords:Privacy protection De-identification Computer vision Video processingWe propose a computer vision-based de-identification pipeline that enables automated protection of pri- vacy of humans in video sequences through obfuscating their appearance, while preserving the natu- ralness and utility of the de-identified data. Our pipeline specifically addresses de-identifying soft and non-biometric features, such as clothing, hair, skin color etc., which often remain recognizable when sim- pler techniques such as blurring are applied. Assuming a surveillance scenario, we combine background subtraction based on Gaussian mixtures with an improved version of the GrabCut algorithm to find and segment pedestrians. De-identification is performed by altering the appearance of the segmented pedes- trians through the neural art algorithm that uses the responses of a deep neural network to render the pedestrian images in a different style. Experimental evaluation is performed both by automated classifi- cation and through a user study. Results suggest that the proposed pipeline successfully de-identifies a range of hard and soft biometric and non-biometric identifiers, including face, clothing and hair.© 2017 Elsevier Ltd. All rights reserved. Keywords: Privacy protection | De-identification | Computer vision | Video processing |
مقاله انگلیسی |
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Privacy-preserving multi-hop profile-matching protocol for proximity mobile social networks
حفظ حریم شخصی پروتکل تطبیق مشخصات چند هاب برای نزدیکی شبکه های اجتماعی سیار-2017 Proximity-based mobile social networks (PMSNs) enable users to easily discover and foster social
interactions with others through user-profile matching. The user profiles in PMSNs contain sensitive
personal information and an occasional leak will violate people’s privacy. Hence, it is a major concern.
In this paper, we propose a privacy-preserving multi-hop profile-matching protocol for PMSNs. The
proposed protocol allows users to customize their own matching preference and to make the matching
results more precise. Unlike the state-of-the-art profile matching approaches that focus only within a
single-hop, the proposed approach makes profile matching within several hops. Moreover, analysis of the
security and performance indicates that the proposed protocol achieves secure and privacy-preserving
friend discovery with higher efficiency. It executes in less time and consumes less energy than other
related protocols.
Keywords: Profile-matching | Friend discovery | Privacy-preserving |Dot product |
مقاله انگلیسی |
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The presence and future of the use of DNA-Information and the protection of genetic informational privacy: A comparative perspective
حضور و آینده استفاده از اطلاعات DNA و حفاظت از حریم خصوصی اطلاعات ژنتیکی: یک چشم انداز مقایسه ای-2016 DNA-Information is used to solve a criminal case or to establish a match between the suspect of a particular criminal case and other unsolved crimes. However, DNA-Information on its own is not entirely reliable evidence as this scientific technology produces errors with a certain probability. The use of DNA- Information in criminal proceedings is in conflict with the protection of individuals genetic informational privacy. Although England and Wales, Germany and South Korea have different legal provisions on the use of DNA-Information, in all these legal orders there are similar problems. Therefore, there is a need for appropriate legislative criteria which balance the protection of individuals genetic informational privacy relating to DNA and the effectiveness of the criminal justice system in a society which employs all the available modern forensic technologies.© 2015 Elsevier Ltd. All rights reserved.
Keywords: DNA-Information | Genetic informational privacy | Genetic fingerprinting | DNA samples | Biological material | Criminal justice system |
مقاله انگلیسی |
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طبقه بندی داده ها برای دستیابی به امنیت در محاسبات ابری
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 13 داده ها دارایی با ارزش و نگرانی بزرگی در هنگام حرکت به سمت ابر میباشد. حریم خصوصی و امنیت داده، منطقه فعال پژوهشی و آزمایشات در محاسبات ابری است.نشت اطلاعات و حفاظت از حریم خصوصی برای بسیاری از سازمان هایی که در حال حرکت به سمت ابر هستند، مهم است. داده ها میتوانند از انواع مختلفی باشند و همچنین درجه حفاظت مورد نیاز برای تمامی داده ها نیز متفاوت است.در اینجا ما یک روش طبقه بندی را پیشنهاد میکنیم که پارامترهای مختلفی را تعریف میکند.پارامترها بر اساس ابعاد مختلفی تعریف شده اند.امنیت داده ها را میتوان بر اساس سطح و حفاظت مورد نیازشان ارائه داد.مقررات امنیتی در ذخیره سازی را میتوان بر اساس مجموعه داده های طبقه بندی شده اعمال کرد. بهره وری طرح طبقه بندی استفاده شده با داده های نمونه جمع آوری شده تحلیل شده است.
کلمات کلیدی: محاسبات ابری | اطلاعات | طبقه بندی داده ها | امنیت
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