Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training. Massive data collection required for deep learning presents obvious privacy issues. Users personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete it nor restrict the purposes for which it is used. So, data privacy has been a very important concern for governments and companies these days. It gives rise to a very interesting challenge since on the one hand, we are pushing further and further for high-quality models and accessible data, but on the other hand, we need to keep data safe from both intentional and accidental leakage. The more personal the data is it is more restricted it means some of the most important social issues cannot be addressed using machine learning because researchers do not have access to proper training data. But by learning how to machine learning that protects privacy we can make a huge difference in solving many social issues like curing disease etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, we introduce differential privacy, which ensures that different kinds of statistical analyses dont compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.
Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors …
Seoul National University（首尔大学）；Ehwa University（梨花大学）
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided based on when they occur: if an attack occurs during training, it is known as a poisoning attack, and if it occurs during inference (after training) it is termed an evasion attack. Poisoning attacks compromise the training process by corrupting the data with malicious examples, whil…
FINATRAX, University of Luxembourg（卢森堡大学FINATRAX）；University of Luxembourg（卢森堡大学）
With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential l…
University of Technology Sydney（悉尼科技大学）；City University of Macau（澳门城市大学）
Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systemati…
Application markets provide a communication channel between app developers and their end-users in form of app reviews, which allow users to provide feedback about the apps. Although security and privacy in mobile apps are one of the biggest issues, it is unclear how much people are aware of these or discuss them in reviews. In this study, we explore the privacy and security concerns of users using reviews in the Google Play Store. For this, we conducted a study by analyzing around 2.2M reviews from the top 539 apps of this Android market. We found that 0.5\% of these reviews are related to the…
University of Technology Sydney（悉尼科技大学）；Macquarie University（麦考瑞大学）；University of South Wales（南威尔士大学）；Federation University（联邦大学）
Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in…
This paper discusses about the challenges, advantages and shortcomings of existing solutions in data security and privacy in public cloud computing. As in cloud computing, oceans of data will be stored. Data stored in public cloud would face both outside attacks and inside attacks since public cloud provider themselves are untrusted. Conventional encryption could be used for storage, however most data in cloud needs further computation. Decryption before computation will cause large overheads for data operation and lots of inconvenience. Thus, efficient methods to protect data security as well…
This is the Proceedings of the Ninth Workshop on Web 2.0 Security and Privacy (W2SP) 2015, held in San Jose, CA, USA, on May 21, 2015. The workshop was held as part of the IEEE Computer Society Security and Privacy Workshops, in conjunction with the IEEE Symposium on Security and Privacy.
Cloud computing comes with a lot of advanced features along with privacy and security problem. Smart meter data takes the benefit of cloud computing in the smart grid. User's privacy can be compromised by analyzing the smart meter data generated by household electrical appliances. The user loses control over the data while data is shifted to the cloud. This paper describes the issues under the privacy and security of smart meter data in the cloud environment. We also compare the existing approaches for preserving the privacy and security of smart meter data.
For security and privacy management and enforcement purposes, various policy languages have been presented. We give an overview on 27 security and privacy policy languages and present a categorization framework for policy languages. We show how the current policy languages are represented in the framework and summarize our interpretation. We show up identified gaps and motivate for the adoption of policy languages for the specification of privacy-utility trade-off policies.
This paper presents an overview of the current state-of-the-art of security and privacy concerns regarding RFID-based car key applications. We will first present a general overview of the technology and its evolution before moving on to an overview and discussion of the various known security weaknesses and attacks against such systems and the associated privacy risks they introduce.
Universitat Rovira i Virgili（罗维拉-维尔吉利大学）；Universitat Oberta de Catalunya (UOC)（加泰罗尼亚开放大学）；Europe Cloud Service Competence Center, Huawei Technologies Oy (Finland) Co. Ltd（华为技术（芬兰）有限公司欧洲云服务能力中心）
Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues. On the one hand, the model updates sent by the clients at each training epoch might leak information on the clients' private data. On the other hand, the model learnt by the server may be subjected to attacks by malicious clients; these securi…
Recent privacy awareness initiatives such as the EU General Data Protection Regulation subdued Machine Learning (ML) to privacy and security assessments. Federated Learning (FL) grants a privacy-driven, decentralized training scheme that improves ML models' security. The industry's fast-growing adaptation and security evaluations of FL technology exposed various vulnerabilities that threaten FL's confidentiality, integrity, or availability (CIA). This work assesses the CIA of FL by reviewing the state-of-the-art (SoTA) and creating a threat model that embraces the attack's surface, adversarial…
Millions of users across the world leverages data processing and sharing benefits from cloud environment. Data security and privacy are inevitable requirement of cloud environment. Massive usage and sharing of data among users opens door to security loopholes. This paper envisages a discussion of cloud environment, its utilities, challenges, and emerging research trends confined to secure processing and sharing of data.
In this document, we define the NADA security architecture based on refined use case scenarios, a derived high level model and security analysis. For the architecure design and verification we are applying the well known STRIDE model.
This paper is a general survey of all the security issues existing in the Internet of Things (IoT) along with an analysis of the privacy issues that an end-user may face as a consequence of the spread of IoT. The majority of the survey is focused on the security loopholes arising out of the information exchange technologies used in Internet of Things. No countermeasure to the security drawbacks has been analyzed in the paper.
Security issues in processor architectures remain really critical since users and devices continue to share computing as well as networking resources. So, preserving data privacy in such an environment is really a critical concern. We know that there is a continuous growth in security and privacy issues that need to be addressed. Here, we have chosen a microservice architecture, which is a small or even an independent microprocess that interacts, acts, and responds to messages via lightweight technologies such as Thrift, HTTP, or REST API.
Smart city is one of the most known Internet of Things (IoT) applications. The smart city services improve user's daily lives. However, security and privacy issues are slowing down their adoption. In addition, the characteristics of IoT devices, applications and users make security implementation of the considered applications a challenging task. To address these issues, we present, in this paper, a new context-aware security and privacy architecture for the IoT. Thanks to the "as a service" approach, this new architecture will be user-centric. It will also support known context-aware security…
Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy r…
This paper proposes a frictionless authentication system, provides a comprehensive security analysis of and proposes potential solutions for this system. It first presents a system that allows users to authenticate to services in a frictionless manner, i.e., without the need to perform any particular authentication-related actions. Based on this system model, the paper analyses security problems and potential privacy threats imposed on users, leading to the specification of a set of security and privacy requirements. These requirements can be used as a guidance on designing secure and privacy-…
Big Data Sharing (BDS) refers to the act of the data owners to share data so that users can find, access and use data according to the agreement. In recent years, BDS has been an emerging topic due to its wide applications, such as big data trading and cross-domain data analytics. However, as the multiple parties are involved in a BDS platform, the issue of security and privacy violation arises. There have been a number of solutions for enhancing security and preserving privacy at different big data operations (e.g., data operation, data searching, data sharing and data outsourcing). To the be…
The synergy between the cloud and the IoT has emerged largely due to the cloud having attributes which directly benefit the IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: 1) to analyse the different components of Cloud computing and the IoT and 2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propos…
In a previous article for S&P magazine, we made a case for the new intellectual challenges in the Internet of Things security research. In this article, we revisit our earlier observations and discuss a few results from the computer security community that tackle new issues. Using this sampling of recent work, we identify a few broad general themes for future work.
There are growing concerns and anxiety about privacy among the general public especially after the revelations of former NSA contractor and whistleblowers like Edward Snowden and others. While privacy is the fundamental concept of being human, the growing tug-of-war between an individuals privacy and freedom vs national security has renewed the concerns about where the fine balance should lie between the two. For the first time in history the technological advancement has made the mass data gathering, analysis, and storage a financially and technologically feasible option for the governments a…
This paper investigates the security and privacy of Internet-connected children's smart toys through case studies of three commercially-available products. We conduct network and application vulnerability analyses of each toy using static and dynamic analysis techniques, including application binary decompilation and network monitoring. We discover several publicly undisclosed vulnerabilities that violate the Children's Online Privacy Protection Rule (COPPA) as well as the toys' individual privacy policies. These vulnerabilities, especially security flaws in network communications with first-p…
Smart homes are a special use-case of the Internet-of-Things (IoT) paradigm. Security and privacy are two prime concern in smart home networks. A threat-prone smart home can reveal lifestyle and behavior of the occupants, which may be a significant concern. This article shows security requirements and threats to a smart home and focuses on a privacy-preserving security model. We classify smart home services based on the spatial and temporal properties of the underlying device-to-device and owner-to-cloud interaction. We present ways to adapt existing security solutions such as distance-boundin…
Blockchain offers an innovative approach to storing information, executing transactions, performing functions, and establishing trust in an open environment. Many consider blockchain as a technology breakthrough for cryptography and cybersecurity, with use cases ranging from globally deployed cryptocurrency systems like Bitcoin, to smart contracts, smart grids over the Internet of Things, and so forth. Although blockchain has received growing interests in both academia and industry in the recent years, the security and privacy of blockchains continue to be at the center of the debate when depl…
This paper studies the current "identity crisis" caused by the substantial security, privacy and usability shortcomings encountered in existing systems for identity management. Some of these issues are well known, while others are much less understood. This paper brings them together in a single, comprehensive study and proposes recommendations to resolve or to mitigate the problems. Some of these problems cannot be solved without substantial research and development effort.
Website owners make conscious and unconscious decisions that affect their users, potentially exposing them to privacy and security risks in the process. In this paper we introduce PrivacyScore, an automated website scanning portal that allows anyone to benchmark security and privacy features of multiple websites. In contrast to existing projects, the checks implemented in PrivacyScore cover a wider range of potential privacy and security issues. Furthermore, users can control the ranking and analysis methodology. Therefore, PrivacyScore can also be used by data protection authorities to perfor…
Development and growth of Internet of Things (IoT) technology has exponentially increased over the course of the last 10 years since its inception, and as a result has directly influenced the popularity and size of smart homes. In this article we present the main technologies and applications that constitute a smart home, we identify the main security and privacy challenges that smart home face and we provide good practices to mitigate those threats.
Cloud computing is revolutionizing many ecosystems by providing organizations with computing resources featuring easy deployment, connectivity, configuration, automation and scalability. This paradigm shift raises a broad range of security and privacy issues that must be taken into consideration. Multi-tenancy, loss of control, and trust are key challenges in cloud computing environments. This paper reviews the existing technologies and a wide array of both earlier and state-of-the-art projects on cloud security and privacy. We categorize the existing research according to the cloud reference …
Interconnected smart vehicles offer a range of sophisticated services that benefit the vehicle owners, transport authorities, car manufacturers and other service providers. This potentially exposes smart vehicles to a range of security and privacy threats such as location tracking or remote hijacking of the vehicle. In this article, we argue that BlockChain (BC), a disruptive technology that has found many applications from cryptocurrencies to smart contracts, is a potential solution to these challenges. We propose a BC-based architecture to protect the privacy of the users and to increase the…
The online privacy and security of the disabled community is a complex field that has implications for every user who navigates web services. While many disciplines have separately researched the disabled population and their online privacy and security concerns, the overlap between the two is very high but under-researched. Moreover, a complex relationship exists between the disabled population and web services where the interaction depends on several web service developmental factors, including usability and accessibility. To this aid, we explored this intersection of privacy and security of…
Employees are often required to use Enterprise Security Software ("ESS") on corporate and personal devices. ESS products collect users' activity data including users' location, applications used, and websites visited - operating from employees' device to the cloud. To the best of our knowledge, the privacy implications of this data collection have yet to be explored. We conduct an online survey (n=258) and a semi-structured interview (n=22) with ESS users to understand their privacy perceptions, the challenges they face when using ESS, and the ways they try to overcome those challenges. We fou…
The Internet of Things (IoT) is intended for ubiquitous connectivity among different entities or "things". While its purpose is to provide effective and efficient solutions, security of the devices and network is a challenging issue. The number of devices connected along with the ad-hoc nature of the system further exacerbates the situation. Therefore, security and privacy has emerged as a significant challenge for the IoT. In this paper,we aim to provide a thorough survey related to the privacy and security challenges of the IoT. This document addresses these challenges from the perspective o…
We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in VSNs towards standardization and deployment. We note that beyond safety applications that have drawn a lot of attention in VC systems, there is significant and rising interest in vehicle-to-vehicle interaction for a range of transportation efficiency and infotainment applications, notably LBS as well as a gamut of services by mobile providers. While this enriches the VC systems and the user exp…
A lot of research has been focused on secure outsourcing of biometric identification in the context of cloud computing. In such schemes, both the encrypted biometric database and the identification process are outsourced to the cloud. The ultimate goal is to protect the security and privacy of the biometric database and the query templates. Security analysis shows that previous schemes suffer from the enrolment attack and unnecessarily expose more information than needed. In this paper, we propose a new secure outsourcing scheme aims at enhancing the security from these two aspects. First, bes…
Increased levels of digitalization in society expose companies to new security threats, requiring them to establish adequate security and privacy measures. Additionally, the presence of exogenous forces like new regulations, e.g., GDPR and the global COVID-19 pandemic, pose new challenges for companies that should preserve an adequate level of security while having to adapt to change. In this paper, we investigate such challenges through a two-phase study in companies located in Denmark -- a country characterized by a high level of digitalization and trust -- focusing on software development a…
The Solid specification aims to empower data subjects by giving them direct access control over their data across multiple applications. As governments are manifesting their interest in this framework for citizen empowerment and e-government services, security and privacy represent pivotal issues to be addressed. By analyzing the relevant legislation, notably GDPR, and international standards, namely ISO/IEC 27001:2011 and 15408, we formulate the primary security and privacy requirements for such a framework. Furthermore, we survey the current Solid protocol specifications regarding how they c…
In [3], the authors proposed a highly efficient secure and privacy-preserving scheme for secure vehicular communications. The proposed scheme consists of four protocols: system setup, protocol for STP and STK distribution, protocol for common string synchronization, and protocol for vehicular communications. Here we define the security models for the protocol for STP and STK distribution, and the protocol for vehicular communications,respectively. We then prove that these two protocols are secure in our models.
Micromobility vehicles are gaining popularity due to their portable nature, and their ability to serve short distance urban commutes better than traditional modes of transportation. Most of these vehicles, offered by various micromobility service providers around the world, are shareable and can be rented (by-the-minute) by riders, thus eliminating the need of owning and maintaining a personal vehicle. However, the existing micromobility ecosystem comprising of vehicles, service providers, and their users, can be exploited as an attack surface by malicious entities - to compromise its security…
