scholarly journals Comprehensive Study of Security and Privacy of Emerging Non-Volatile Memories

2021 ◽  
Vol 11 (4) ◽  
pp. 36
Author(s):  
Mohammad Nasim Imtiaz Khan ◽  
Swaroop Ghosh
Keyword(s):  
Data Privacy ◽  
New Technologies ◽  
Spin Transfer Torque ◽  
Main Memory ◽  
Security And Privacy ◽  
System Level ◽  
System Throughput ◽  
Privacy And Security ◽  
Von Neumann ◽  
Privacy Issues

Several promising non-volatile memories (NVMs) such as magnetic RAM (MRAM), spin-transfer torque RAM (STTRAM), ferroelectric RAM (FeRAM), resistive RAM (RRAM), and phase-change memory (PCM) are being investigated to keep the static leakage within a tolerable limit. These new technologies offer high density and consume zero leakage power and can bridge the gap between processor and memory. The desirable properties of emerging NVMs make them suitable candidates for several applications including replacement of conventional memories. However, their unique characteristics introduce new data privacy and security issues. Some of them are already available in the market as discrete chips or a part of full system implementation. They are considered to become ubiquitous in future computing devices. Therefore, it is important to ensure their security/privacy issues. Note that these NVMs can be considered for cache, main memory, or storage application. They are also suitable to implement in-memory computation which increases system throughput and eliminates von Neumann bottleneck. Compute-capable NVMs impose new security and privacy challenges that are fundamentally different than their storage counterpart. This work identifies NVM vulnerabilities and attack vectors originating from the device level all the way to circuits and systems, considering both storage and compute applications. We also summarize the circuit/system-level countermeasures to make the NVMs robust against security and privacy issues.

scholarly journals Enabling privacy and security in Cloud of Things: Architecture, applications, security & privacy challenges

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ado Adamou Abba Ari ◽  
Olga Kengni Ngangmo ◽  
Chafiq Titouna ◽  
Ousmane Thiare ◽  
Kolyang ◽  
...  
Keyword(s):  
Data Privacy ◽  
Security And Privacy ◽  
Growing Up ◽  
Privacy And Security ◽  
Privacy Concerns ◽  
Security Issues ◽  
Important Amount ◽  
Iot Devices ◽  
Privacy Issues ◽  
The Internet Of Things

The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.

Security, Privacy, and Trust in Mobile Systems and Applications

2011 ◽  
pp. 312-341 ◽  
Author(s):  
Marco Cremonini ◽  
Ernesto Damiani ◽  
Sabrina De Capitani di Vimercati ◽  
Angelo Corallo ◽  
Gianluca Elia
Keyword(s):  
Information Security ◽  
Mobile Systems ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Privacy Issues

Mobile systems and applications are raising some important information security and privacy issues. This chapter discusses the need for privacy and security in mobile systems and presents technological trends which highlight that this issue is of growing concern.

Security and Privacy in Big Data Computing

2020 ◽  
pp. 236-256
Author(s):  
Kiritkumar J. Modi ◽  
Prachi Devangbhai Shah ◽  
Zalak Prajapati
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Privacy And Security ◽  
Cloud Data ◽  
Big Data Privacy ◽  
Cloud Data Storage ◽  
Type Data ◽  
Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.

A Survey of Authentication Schemes in the Internet of Things

2019 ◽  
Vol 6 (1) ◽  
pp. 15-30 ◽  
Author(s):  
Yasmine Labiod ◽  
Abdelaziz Amara Korba ◽  
Nacira Ghoualmi-Zine
Keyword(s):  
Internet Of Things ◽  
Security Requirements ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy And Security ◽  
Depth Study ◽  
Authentication Schemes ◽  
Iot Devices ◽  
Privacy Issues ◽  
The Internet Of Things

In the recent years, the Internet of Things (IoT) has been widely deployed in different daily life aspects such as home automation, electronic health, the electric grid, etc. Nevertheless, the IoT paradigm raises major security and privacy issues. To secure the IoT devices, many research works have been conducted to counter those issues and discover a better way to remove those risks, or at least reduce their effects on the user's privacy and security requirements. This article mainly focuses on a critical review of the recent authentication techniques for IoT devices. First, this research presents a taxonomy of the current cryptography-based authentication schemes for IoT. In addition, this is followed by a discussion of the limitations, advantages, objectives, and attacks supported of current cryptography-based authentication schemes. Finally, the authors make in-depth study on the most relevant authentication schemes for IoT in the context of users, devices, and architecture that are needed to secure IoT environments and that are needed for improving IoT security and items to be addressed in the future.

scholarly journals A survey on fog computing: research challenges in security and privacy issues

2018 ◽  
Vol 7 (2.7) ◽  
pp. 335 ◽  
Author(s):  
T Veerraju ◽  
Dr K. Kiran Kumar
Keyword(s):  
Fog Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
Limited Resources ◽  
Privacy And Security ◽  
Computing Paradigm ◽  
Is Security ◽  
Real Time Applications ◽  
And Storage ◽  
Privacy Issues

With the rapid advancement of Internet of Things has enabled to combine the intercommunication and interconnection between seamless networks. Cloud computing provides backend solutions and one among the most prominent technologies for the users, still cannot be solved all the problems such as latency of real time applications. However, a new computing paradigm comes in to the picture. Many of the researchers focused on this exemplar known as Fog/Edge computing, which has been planned to the extension of cloud services. Fog provides the services to the edge of the networks, which makes communication, computation and storage for end users through fog devices and for servers like controllers. We analyze the study, which aims to augment low bandwidth, latency along with the privacy and security.   The major problem in the Fog computing is security due to the limited resources. In this paper, we investigated the protection issues and confrontation of Fog and also provide countermeasures on security for different attacks. We focused the future security directions and challenges to address in fog networks.

Privacy Dangers of Wearables and the Internet of Things

2016 ◽  
pp. 379-402 ◽  
Author(s):  
Scott Amyx
Keyword(s):  
Data Protection ◽  
Data Privacy ◽  
Security And Privacy ◽  
Employee Privacy ◽  
Privacy Concerns ◽  
Business Operations ◽  
Managerial Implications ◽  
Iot Devices ◽  
Privacy Issues ◽  
The Internet Of Things

This chapter identifies concerns about, and the managerial implications of, data privacy issues related to wearables and the IoT; it also offers some enterprise solutions to the complex concerns arising from the aggregation of the massive amounts of data derived from wearables and IoT devices. Consumer and employee privacy concerns are elucidated, as are the problems facing managers as data management and security become an important part of business operations. The author provides insight into how companies are currently managing data as well as some issues related to data security and privacy. A number of suggestions for improving the approach to data protection and addressing concerns about privacy are included. This chapter also examines trending issues in the areas of data protection and the IoT, and contains thought-provoking discussion questions pertaining to business, wearables/IoT data, and privacy issues.

Data Privacy vs. Data Security

2014 ◽  
pp. 215-234
Author(s):  
Sue Milton
Keyword(s):  
Data Security ◽  
Data Privacy ◽  
Identity Management ◽  
Data Access ◽  
Privacy And Security ◽  
Access Policy ◽  
Data Usage ◽  
And Performance ◽  
The Relationship ◽  
Privacy Issues

This chapter assumes data is a key asset that, if lost or damaged, severely disrupts business capability and reputation. The chapter has one core purpose, to provide leaders with sufficient understanding of two data management fundamentals, data privacy and data security. Without that understanding, Information Technology (IT) security will always be seen as a cost on, not an investment towards, quality and performance. The chapter reviews the relationship between data privacy and data security. It argues that data security cannot be achieved until data privacy issues have been addressed. Simply put, data privacy is fundamental to any data usage policy and data security to the data access policy. The topic is then discussed in broader terms, in the context of data and information management, covering various themes such as cyber-crime, governance, and innovations in identity management. The chapter's intended outcome is to clarify the relationship between data privacy and security and how this understanding helps reduce data abuse. The link between privacy and security will also demystify the reason for high costs in implementing and maintaining security policies and explain why leaders need to provide stronger IT strategic leadership to ensure IT investment is defined and implemented wisely.

Security and Privacy Issues in Smart Cities

2021 ◽  
pp. 64-75
Author(s):  
Aditya Sam Koshy ◽  
Nida Fatima ◽  
Bhavya Alankar ◽  
Harleen Kaur ◽  
Ritu Chauhan
Keyword(s):  
Smart Cities ◽  
Social Systems ◽  
Well Being ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Subsequent Work ◽  
Economic Changes ◽  
City Structure ◽  
Privacy Issues ◽  
Major Emphasis

The world is going through growth in smart cities, and this is possible because of a revolution of information technology contributing towards social and economic changes and hence endowing challenges of security and privacy. At present, everything is connected through internet of things in homes, transport, public progress, social systems, etc. Nevertheless, they are imparting incomparable development in standard of living. Unified structure commits to welfare, well-being, and protection of people. This chapter surveys two consequential threats, that is, privacy and security. This chapter puts forward review of some paperwork done before consequently finding the contributions made by author and what subsequent work can be carried out in the future. The major emphasis is on privacy security of smart cities and how to overcome the challenges in achievement of protected smart city structure.

Efficient top representative for multi-authorship encrypted cloud data to assist cognitive search

2020 ◽  
Vol 39 (6) ◽  
pp. 8079-8089
Author(s):  
P. Shanthi ◽  
A. Umamakeswari
Keyword(s):  
Cloud Storage ◽  
Data Privacy ◽  
New Technologies ◽  
Keyword Search ◽  
Information Leakage ◽  
Cloud Services ◽  
Security And Privacy ◽  
Business World ◽  
Cloud Data ◽  
Privacy And Confidentiality

Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.

scholarly journals Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

2020 ◽  
Vol 2020 (2) ◽  
pp. 5-23
Author(s):  
Sergiu Carpov ◽  
Caroline Fontaine ◽  
Damien Ligier ◽  
Renaud Sirdey
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Use Cases ◽  
Security And Privacy ◽  
Functional Encryption ◽  
Cryptographic Primitives ◽  
Encrypted Data ◽  
Current State ◽  
Encryption Schemes ◽  
Privacy Issues

AbstractClassification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear.In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.

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