Abstract

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment.

Introduction

In the post-pandemic era, telecommuting has become an indispensable working mode, and emerging technologies such as cloud computing have played an important role in epidemic monitoring, prevention and control and medical assistance, showing broader application prospects and growth potential [1], [2]. According to Gartner, from 2015 to 2020, the penetration rate of global cloud computing market represented by IaaS, PaaS and SaaS increased year by year from 4.3% to 13.1%, and will rise to 15.3% in the next year. The market size is 208.3 billion dollars in 2020 and will exceed 600 billion dollars in 2025 [3].

Cloud services can eliminate the storage space constraints of personal devices and reduce local computing overhead. In practice, different companies or organizations store data in the cloud and use the cloud to share data with other members. Designing a one-to-many data sharing scheme based on attribute-based encryption(ABE) [4], [5] can achieve effective access control, but ABE is not suitable for collaborative computing between multiple members in a cloud environment. At the same time, multiple clients want to use the computing power of the cloud server to perform machine learning and data mining, collaboratively solve optimization problems and obtain optimal results. However, uploading user data to the server and training the model on the server may lead to serious user privacy disclosure. For example, in 2021, the registration information of Alibaba Cloud users was leaked, in 2022, 37 GB source code of Microsoft was leaked, and 170 million data of super star learning APP was illegally sold. The losses caused by global cybercrime more than 6 trillion in 2021, about six times as much as in 2020. With frequent privacy disclosure, the public pays more attention to privacy protection. In order to make better use of cloud services, we need a method to process data safely in the cloud environment.

Conclusion

Considering the diversity of users in the cloud computing environment, MKHE supports information storage and sharing and secure homomorphic calculation from different users. It is more suitable to solve privacy and security issues in the cloud computing, which is of great practical significance. However, the existing MKHE schemes weaken the functionality of MKHE in the expansion of ciphertext dimension and computational complexity. In this paper, we first improve the DGHV scheme [11] and propose a DGHV-type MKHE scheme. Compared with the original DGHV scheme, on the premise of ensuring security, an extended key is introduced to expand the function of the scheme. Compared with other MKHE schemes, our scheme is easier to understand in ciphertext composition and calculation form. In addition, in the experiment, the cubic form is used to reduce the public key size of the scheme to ?(? 3 ). Through experiments, we evaluate the generation time of the keys, storage capacity, expansion and homomorphic operation. However, introducing a trusted KGC when generating joint key may be difficult to meet in some practical applications. Whether to cancel the setting of KGC in joint key distribution is an important research content. We should also strive to make MKHE better applicable to the cloud environment.

Highlights

Abstract

Keywords

1. Introduction

2. Theoretical background

3. Conceptual model and hypothesis development

4. Methodology

5. Data analysis

6. Discussion

7. Conclusion

Authorship statement

Declaration of Competing Interest

Acknowledgements

Appendix A: Study measurement

References

Vitae

Abstract

There are numerous risks associated with the interconnection of healthcare provision and the Internet of Things (IoT), with its sensory capabilities shown to reduce confidence in novel technology due to fears of a loss of privacy. There exists a clear omission in the extant literature—consideration of gender differences in Frontline Healthcare Providers’ (FHP) behavioural intentions—which this work aims to address through the analysis of IoT-enabled healthcare applications’ (HAs) behavioural intentions in multicultural and bi-generational (Gen X, Y) context. Essentially, analysing gender and generational differences in relation to the variables (privacy, security and trust that influence risk perception; the latter alongside attitude and perceived behavioural control potentially affect the intention) affecting FHPs’ BI towards IoT enabled HAs. A novel model is presented herein, which combines Planned Behaviour (TPB), Privacy Calculus (PCT), and the trust-risk framework. Questionnaire methodology (n = 401) was applied to both generations under consideration, data was assessed using Partial Least Squares Multi-Group Analysis (PLS-MGA), which showed gender differences in Gen Y, but there was little evidence to suggest that risk perception affects any of the cohort’s behavioural intention towards the use of IoT-enabled HA, which in turn should help guide both future institutional policy and application development .

1. Introduction

Advances in network technologies, combined with mass production of smart devices equipped with sensors with continuous, bidirectional transmission, and the advent of cloud computing have been the primary drivers behind IoT development and implementation in big data driven infrastructural control (Hassan et al., 2018; Li et al., 2020; Rafique et al., 2020; Razzak et al., 2020). However, the security problems inherent in the wider internet itself remain prevalent in IoT. In truth, each element in IoT’s tri-layer structure—perception, transport, and application—requires individual consideration in that respect (Tewari and Gupta, 2020). IoT is now commonplace in modern society, often appearing in the food supply chain, logistics, mining, computing, and healthcare sectors (Pang et al., 2015; Yildirim and Ali-Eldin, 2019). Particularly in the case of the latter, the drive for service improvement has resulted in a broad body of literature considering this advance (Asif-Ur-Rahman et al., 2019; Syed et al., 2019).

The interconnected nature of the IoT enabled healthcare model (Tewari and Gupta, 2020) contains a multitude of risks relating to privacy, security and loss of trust. Limitations on the computational ability of this model means that conventional measures used to tackle security and privacy concerns often cannot be applied (Li et al., 2020), leading to the development of a concept known as the Internet of Medical Things, wherein patient data confidentiality without loss of functionality is held paramount (Li et al., 2020).

Abstract

The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive volumes of data explosion. The generated data is collected and shared using edge-based IoT structures at a considerably high frequency. Thus, the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes. To address the identified issue, we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme. In particular, we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes, where IoT devices and edge nodes are two parties of the game. IoT devices may make malicious requests to achieve their goals of stealing privacy. Accordingly, edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed. They dynamically adjust their own strategies according to the opponent’s strategy and finally maximize the payoffs. Built upon a developed application framework to illustrate the concrete data sharing architecture, a novel algorithm is proposed that can derive the optimal evolutionary learning strategy. Furthermore, we numerically simulate evolutionarily stable strategies, and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme. Therefore, the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

Introduction

The Internet of Things (IoT) can be described as a network that connects all entities with the internet through information sensing devices to realize the function of intelligent identification, operation, and management. The IoT is attracting considerable attention with the continuous development of wireless communications, radio frequency identification, and low-cost sensors. However, IoT network problems, such as security and privacy, are rapidly emerging, and thus, privacy protection is of paramount importance [[1], [2], [3], [4], [5]].

     Edge-based IoT [6] is experiencing rapid growth because traditional cloud computing is unable to immediately handle the massive data generated by edge nodes with the rapid development and wide application of the IoT, big data, and 5G/6G networks [7]. In this architecture, edge computing provides parts of cloud services for IoT devices on the edge of the network. It focuses on solving the problems of high latency, network instability, and low bandwidth [8]. Its applications are initiated on the edge side, resulting in the faster response of cloud services, which meets the basic IoT requirements in real-time business, application intelligence, and privacy preservation.

Conclusion and future work

In the current work, we have proposed an edge computing-oriented and evolutionary game-based privacy preservation model to acquire the optimal learning strategy for IoT data sharing. In our scheme, the edge nodes first assess whether the request is normal or malicious and then react with action grants or denies when data is released from the cloud storage system. Under this circumstance, malicious requests can be precisely identified and effectively prohibited from the source. Furthermore, we have analyzed the stability of each equilibrium point via the replication dynamic equations and raised a framework and an algorithm for this model, optimizing the expected gain and receiving the best evolutionary strategy. Additionally, the relevant experimental simulations verify that our scheme is superior from the perspectives of reliability and privacy preservation.

     For future work, we will focus on other game models, such as signaling games and repeated games, to handle privacy preservation during IoT data sharing. In addition, we will take the privacy preservation of a data sender into consideration instead of a data receiver, minimizing the probability of IoT nodes sending malicious requests. Furthermore, it is highly likely to incur malicious attacks in the process of merging data from different IoT devices. Therefore, privacy preservation under IoT data aggregation is another direction with great promise.

Abstrac

1. Introduction

2. Related Work

3. Design and Development of Blockchain-Based IoV

4. Experiment Setup and Results

5. Conclusions

References

Abstract

     The rapid advancement in the area of the Internet of Vehicles (IoV) has provided numerous comforts to users due to its capability to support vehicles with wireless data communication. The exchange of information among vehicle nodes is critical due to the rapid and changing topologies, high mobility of nodes, and unpredictable network conditions. Finding a single trusted entity to store and distribute messages among vehicle nodes is also a challenging task. IoV is exposed to various security and privacy threats such as hijacking and unauthorized location tracking of smart vehicles. Traceability is an increasingly important aspect of vehicular communication to detect and penalize malicious nodes. Moreover, achieving both privacy and traceability can also be a challenging task. To address these challenges, this paper presents a blockchain-based efficient, secure, and anonymous conditional privacy-preserving and authentication mechanism for IoV networks. This solution is based on blockchain to allow vehicle nodes with mechanisms to become anonymous and take control of their data during the data communication and voting process. The proposed secure scheme provides conditional privacy to the users and the vehicles. To ensure anonymity, traceability, and unlinkability of data sharing among vehicles, we utilize Hyperledger Fabric to establish the blockchain. The proposed scheme fulfills the requirement to analyze different algorithms and schemes which are adopted for blockchain technology for a decentralized, secure, efficient, private, and traceable system. The proposed scheme examines and evaluates different consensus algorithms used in the blockchain and anonymization techniques to preserve privacy. This study also proposes a reputation-based voting system for Hyperledger Fabric to ensure a secure and reliable leader selection process in its consensus algorithm. The proposed scheme is evaluated with the existing state-of-the-art schemes and achieves better results.

Introduction

     Internet of Vehicles (IoV) networks are able to improve driving safety, efficiency, and traffic management using On-Board Units (OBUs) for data communication, with or without prior infrastructure. As a result of the increase in the number of users and the open nature of these networks, security threats are a challenge. Security requirements such as authentication, the privacy of vehicle nodes, and audibility are necessary to avoid these networks from different types of attacks, such as impersonation attacks, and spreading of false information. Authentication of nodes in a network is the first line of defense to block any unwanted activity in a network [1,2]. If the network allows unauthenticated vehicle nodes, then malicious vehicle nodes can also join the network and undertake different types of activities, e.g., impersonate an ambulance to exceed the given speed limits. If integrity is not provided during message transmission, then vehicle nodes can misbehave and alter the content of a message. In such a case, the receiver only knows that the message was sent by a legitimate vehicle, and they would be responsible for any damage. Privacy is a core feature of IoV, but traceability is also necessary in the case of any unwanted activity in a network. In this case, the privacy of the vehicle should be revoked and punished

Conclusions

     In this paper, we introduced a hard security solution, i.e., the improved Hyperledger Fabric, to implement a blockchain-enabled IoV for safe vehicle information sharing. This paper comprehensively highlighted the issues related to IoV. The literature review concluded that most of the security services can be achieved by the implementation of blockchain. Hyperledger Fabric is one of the major implementations of blockchain for achieving security services. This paper provides a brief introduction to IoV, Hyperledger Fabric, consensus algorithms, privacy, and anonymization techniques, in conjunction with the additional terminology necessary to understand the problem statement and proposed scheme. Then a critical analysis is undertaken of existing consensus algorithms and conditional privacy schemes in the context of the IoV environment. The paper also discusses the non-applicability of existing schemes to the IoV environment. Thus, there is a requirement for an efficient decentralized scheme for IoV that can address security issues and fulfill the latest security requirements of vehicular communication. Hyperledger Fabric appears to be the most suitable emerging solution for a resource-constrained environment, and addresses some of the functionality issues of IoV. The security analysis indicates the proposed scheme will address some of the limitations of existing schemes and fulfill the security criteria of CPPA schemes for IoV. The two main contributions of this research relate to the manner in which it addresses the issue of the leader selection process. The first is to select leaders based on their reputation. A reputation-based scheme is utilized to calculate the accurate reputation of RSUs. Second, this paper presents an anonymous and traceable CPPA approach that can be utilized in a vehicular network.

Abstract

Keywords

1. Introduction

2. Overview and preliminary preparation of blockchain

3. Blockchain attack classification system

4. Mining attack and defense methods of mining pool

5. Attack and defense methods of network communication and smart contracts

6. Attack and defense methods of privacy thefts

7. Future research direction

8. Conclusion

Declaration of Competing Interest

Acknowledgment

References

Abstract

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.

1. Introduction

Recently, some essential characteristics of blockchain1, such as decentralization, collective maintenance, and immutability, were identified and have led to its explosive growth. Blockchain has been defined as the fifth disruptive innovation of the computing paradigm after the mainframe, personal computer, internet, and mobile and social network [1]. Because the nodes in the blockchain follow the same accounting transaction rules and consensus under the consensus algorithm, adopt the one-way hash algorithm, and strictly generate blocks in chronological order, blockchain has the advantages of immutability and encryption security. Therefore, blockchain is applied to digital currency [2], smart finance [[3], [143]], smart homes [4], smart medical care [5], smart human resource [142], smart transportation [6], [7], and so on. International data corporation (IDC) reported that although the blockchain market worldwide was affected by COVID-19 in 2020, the global spending on blockchain solutions was nearly $4 billion US dollars. As the economy recovers, the rate of global blockchain market spending in 2023 will usher in a strong rebound and the global spending on blockchain solutions will reach $16 billion US dollars [8].

Abstract

     Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.

Introduction

     Bitcoin and its blockchain structure proposed in 2008 [1] caused a new era to be opened in digital cash systems with the concept of proof of work and conversion of mining power into money. Since then, although, many blockchain-based digital currencies came out, Bitcoin still remains at the top of the market with $1,171,005,836,167 market capitalization [2] as of November 2021. Since Bitcoin has a public blockchain and transactions are explicitly visible, activities of the users can be tracked and linked, and the user identities can be revealed by linking one of the transactions to off-network information as surveyed in [3] and [4]. For instance, with the knowledge that someone shopped online for 0.000381 BTC from a well-known e-commerce site, Bitcoin addresses that made a 0.000381 BTC valued shopping can be found by querying the Bitcoin address of the site and the transaction amounts equal to 0.000381 from the blockchain. Consequently, room for research came up for anonymity and privacy improvement in Bitcoin, and many academic papers have been published [3]–[7]. In these studies, generally used methods for anonymity and privacy improvement include mixing protocols, ring signatures, zero-knowledge proof, homomorphic commitments, and off-chain storage systems. Some of these studies are implemented, for example, Monero [8] using ring signatures, and Zcash [9] using zero-knowledge proofs are two of the prominent cryptocurrencies.

Conclusion

     In this study, we present an examination of Bitcoin in terms of differential privacy. Our motivation arises from the fact that differential privacy approaches can be used for improving the privacy of the public Bitcoin blockchain. The differential privacy methods offer the prevention of anonymization and privacy breaches by direct queries and the preservation of checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation using the differential privacy formulation. Then, we examine the application of noise addition to transaction amounts and user graph perturbation as differential privacy mechanisms. Furthermore, we demonstrate an empirical study for practical utilization of the noise addition approach and compare four differential privacy mechanisms according to mean absolute error for varying ε and δ values. In addition, we introduce a new metric called mean ranking offset and use it for the comparison, as well. In Section VII, we summarize our observations. It is observed that the noise addition and the graph perturbation mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. The noise addition method decreases the fraction of the cases violating differential privacy by half for the three query functions, whereas the graph perturbation method decreases the fraction of the cases violating differential privacy by half for all of the four query functions considered.

Abstract

     The information age set off a revolution for organizations and institutions without precedent. Big production of data is a major competitive advantage for companies. Therefore, there are great economic incentives to monetize such data. However, there is evidence that indicates clear vulnerability in terms of user privacy, thereby setting a new paradigm in the search for a balance between privacy and security. This article offers a brief history of privacy to this day, deepens the scope and importance of its standardization in the regulation and future of digital markets.

The Right to Privacy in the Digital Age

     On December 18, 2013, the General Assembly of the United Nations approved the resolution entitled “The Right to Privacy in the Digital Age” for all people (United Nations 2017). This resolution establishes that indiscriminate global surveillance implies a serious violation of human rights and seeks to reaffirm the fundamental principles adopted in the Universal Declaration of Human Rights of 1948 (United Nations General Assembly 1948, article 12), the International Covenant on Civil and Political Rights (Joseph and Castan 2013, article 17), and the International Covenant on Economic, Social, and Cultural Rights (Saul, Kinley, and Mowbray 2014). In particular, this resolution makes it clear that “unlawful or arbitrary surveillance and/or interception of communications, as well as the unlawful or arbitrary collection of personal data, as highly intrusive acts, violate the right to privacy, can interfere with other human rights, including the right to freedom of expression and to hold opinions without interference, and the right to freedom of peaceful assembly and association, and may contradict the tenets of a democratic society.”

Conclusion

     The digital economy plays a fundamental role in the world economy and has been the subject of study by many academics and non-academics for some years. Its real impact on the growth of countries, although it could be incompletely and/or imprecisely measured, points to its growing importance as an engine of economic growth in the upcoming years. However, as it grows in importance, it also faces numerous threats that put its sustainability and functioning at risk, such as digital piracy, violation, and leakage of private data and cybersecurity.

     These threats, which in many cases affect the personal data of millions of users, require some regulation and protection that can establish operating guarantees in the future. Privacy seems to be a moving target and requires an international effort in order to set a common regulation that can enable economic development. Standardization turns out to be crucial and requires an ongoing commitment above any political or eventual issue.

Abstract

     The recent advancements in Internet of Things (IoT) have brought enormous advantages for businesses. These benefits are achieved by services that collect large volumes of data that is collected for analysis. The data may also contain sensitive information. Privacy of such data is an important research challenge. Differential privacy is a recent technique for data privacy. It works by anonymizing the attributes that may contain sensitive information. An essential step before applying differential privacy is the division of attribute set into three groups called sensitive, non-sensitive and ambiguous. A key issue in existing studies is that the division of attribute set is done manually by a domain expert and is therefore costly. We introduce a three-way approach for differential privacy and a supporting algorithm for this demarcation of attribute sets. Results indicate that the information content and stability of the dataset improves considerably with our approach.

Introduction

     Internet of things (IoT) has enormous usage potential in smart homes, Industrial IoTs and medical or healthcare IoTs. This requires collection of large amounts of data that may be stored and shared for analysis. The collected data may contain medical, financial or personal information and its leakage may lead to privacy issues. Many privacy related incidents have been reported in the recent past which demand for efficient and effective solutions for data privacy [1]. On the other hand, in IoT smart homes, there is a greater risk of data and identity theft. Data can be used to analyze human activities and may have serious implications such as robberies.

     Different anonymization techniques have been used in IoTs to protect sensitive information. The most common approach is hiding all the sensitive attributes. This however, leads to significant loss in utility of the data for useful analysis [2]. Another common approach is to release only aggregate values [3]. A privacy breach can occur with this approach if someone manages to gain enough aggregate values that provide hints about sensitive data of an individual [3]. Query auditing is another approach for data privacy [4]. It works by comparing the results of the past queries with the current query to determine whether or not responding to the same query will lead to a privacy breach. It can then deny those queries that lead to a privacy breach. It is however argued that query denials can also lead to information leakage. The approach of k-anonymity was introduced in order to address shortcomings of the earlier techniques. It fails in situations where sensitive values in a class lack diversity [5]. l-diversity was a refinement of k-anonymity [6]. It works by making sure that each class of values has enough sensitive values and that the values are distributed evenly. It is not able to prevent attribute disclosure which makes it susceptible to inference attacks [7]. The approach of t-closeness is a refinement of k-anonymity which works by making sure that the distribution of a sensitive attribute in a class is close to the distribution of the sensitive attribute in the overall table. It however is susceptible to re-identification attacks [8].

Conclusion

     The data collected and stored by sensors in Internet of things (IoT) needs to be protected against privacy breaches. Differential privacy is an approach for privacy preservation. Before applying differential privacy, it is necessary to divide the attribute set into three groups known as sensitive, non-sensitive and ambiguous. Existing practices rely on manual division of attributes by a domain expert and are therefore quite costly. We introduce a three-way approach for automatic attribute division for differential privacy. The approach divides the attribute set based on a pair of thresholds and an evaluation function. The configuration of thresholds controls the groupings or divisions of attributes and needs to be configured carefully. To achieve effective thresholds and the resulting grouping of attributes, we introduce an algorithm called 3WADD that automatically determines the thresholds for an effective division of attributes. An architecture that incorporated 3WADD using differential privacy is also presented. The proposed scheme improves the information content and stability of the dataset.

     These results open up new research avenues for exploring more sophisticated methods of three-way decisions for obtaining effective and useful division of attributes for IoT. In particular, different kinds of evaluation functions may be explored depending on the underlying nature of the data and the specific needs of the application at hand. The automated nature of this approach can significantly reduce the cost of privacy preservation in an IoT dataset and thus motivate more organizations and individuals to use IoT to improve their processes.

Abstract

     While there is increasing global attention to data privacy, most of their current theoretical understanding is based on research conducted in a few countries. Prior work argues that people’s cultural backgrounds might shape their privacy concerns; thus, we could expect people from different world regions to conceptualize them in diverse ways. We collected and analyzed a large-scale dataset of tweets about the #CambridgeAnalytica scandal in Spanish and English to start exploring this hypothesis. We employed word embeddings and qualitative analysis to identify which information privacy concerns are present and characterize language and regional differences in emphasis on these concerns. Our results suggest that related concepts, such as regulations, can be added to current information privacy frameworks. We also observe a greater emphasis on data collection in English than in Spanish. Additionally, data from North America exhibits a narrower focus on awareness compared to other regions under study. Our results call for more diverse sources of data and nuanced analysis of data privacy concerns around the globe.

Introduction

     The right to control one’s personal information has gained significant importance lately (Lee et al. 2019). Indeed, 58% of the countries have data protection and privacy legislation, while another 10% have drafted legislation about it (United Nations Conference on Trade and Development 2020). This broad interest is related to the massive amount of personal data collected by information systems and the risk that such information could be wrongly distributed online (Lee et al. 2019).

     The study of information privacy has advanced our understanding of individuals’ concerns regarding organizational practices associated with collecting and using their personal information (Smith et al. 1996). However, a literature review revealed a strong bias towards USA-centered studies across privacy concerns literature and warned about the limitations to generalizability this entails (Belanger ´ and Crossler 2011; Okazaki et al. 2020). The review’s authors hypothesized that individuals from different world regions have diverse cultures, values, and laws, which can, in turn, result in different conceptualizations of information privacy and its impacts (Belanger and Crossler ´ 2011; Mohammed and Tejay 2017). To study these differences, privacy research has often relied on survey-based studies (Cockcroft and Rekker 2016). For example, a questionnaire was applied to explore differences in privacy perceptions between Facebook users from Germany and the USA (Krasnova and Veltri 2010), and a cross-national survey was conducted to evaluate information attitudes of consumers in the USA and Brazil (Markos et al. 2017). These multi-country privacy studies have had limited sample sizes, which makes the results difficult to generalize (Lee et al. 2019; Huang and Bashir 2016). They also tend to be focused on one or two cultures, usually including the USA (Cockcroft and Rekker 2016). Hence, multi-country information privacy research is still needed to extend our understanding of this increasingly relevant topic around the globe (Adu et al. 2019; Zou et al. 2018).

Conclusion

     We conducted a cross-language and cross-regional study on social media content about a major data privacy leakage: the Cambridge Analytica scandal. We categorized our Twitter data into two different languages and four geographical regions.

     Our results shed light on language and regional differences on information privacy concerns by 1) creating word embeddings by language and world regions to leverage social media data about a data breach scandal, 2) conducting open coding and content analysis of the semantic contexts (generated by the embeddings) of privacy-related keywords, 3) mapping the results to a well-known information privacy framework, and (4) conducting a comparative analysis across two languages and four world regions.

     We found that data in English shows a broader emphasis on data collection , while data from North America shows the smallest emphasis on awareness. In turn, data from Latin America has the broadest emphasis on awareness. We discuss how our findings extend current conceptualizations of information privacy concerns, and how they might relate to regulations about personal data usage in the regions we analyzed.

Abstract

     Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

Introduction

     Artificial Intelligence (AI) is one of the most prevalent topics of research today across almost every scientific field. For example, multi-agent systems can be applied to distributed control systems [1], while distributed machine learning has been adopted by Google for mobile users [2]. However, as AI becomes more and more reliant on data, several new problems have emerged, such as privacy violations, security issues, model instability, model fairness and communication overheads. As just a few of the tactics used to derail AI, adversarial samples can fool machine learning models, leading to incorrect results. Multi-agent systems may receive false information from malicious agents. As a result, many researchers have been exploring new and existing security and privacy tools to tackle these new emerging problems. Differential privacy is one of these tools.

     Differential privacy is a prevalent privacy preservation model which guarantees whether an individual’s information is included in a dataset has little impact on the aggregate output. Fig. 1 illustrates a basic differential privacy framework using the following example. Consider two datasets that are almost identical but differ in only one record and that, access to the datasets is provided via a query function f. If we can find a mechanism that can query both datasets and obtain the same outputs, we can claim that differential privacy is satisfied. In that scenario, an adversary cannot associate the query outputs with either of the two neighbouring datasets, so the one different record is safe. Hence, the differential privacy guarantees that, even if an adversary knows all the other records in a dataset except for one unknown individual, they still cannot infer the information of that unknown record.

Conclusion

     In this paper, we investigated the use of differential privacy in selected areas of AI. We described the critical issues facing AI and the basic concepts of differential privacy, highlighting how differential privacy can be applied to solving some of these problems. We discussed the strengths and limitations of the current studies in each of these areas and also pointed out the potential research areas of AI where the benefits of differential privacy remain untapped. In addition to the three areas of focus in this article – machine learning, deep learning and multi-agent learning – there are many other interesting areas of research in AI that have also leveraged differential privacy, such as natural language processing, computer vision, robotics, etc. Surveying differential privacy in these areas is something we intend to do in future work.

Abstract

     Significant amounts of data are collected in buildings. While these data have great potential for maximizing the energy efficiency of buildings in general, only a small portion of the data are accessible to researchers, government, and industry for analyses. Concerns about privacy are one of the major barriers prohibiting access to these data. Privacy preservation techniques are generally applied to this problem not only to preserve underlying privacy but also to improve the usefulness of data. Among various privacy preserving techniques, differential privacy has become one of the more popular solutions since its introduction in 2006. Differential privacy is a mathematical measure for protecting privacy so that one’s privacy cannot be incurred by participating in a database. Although significant research improvements have been made for more than a decade, applying differential privacy to data collected in buildings is still an immature field of study. Because implementing differential privacy on a certain use case is not straightforward and can be achieved with various configurations, it is important to understand variation of configurations with different use cases around data collected from buildings. This literature review aims to introduce what has been done to implement differential privacy in data collected in buildings, and to discuss associated challenges and potential future research opportunities.

Introduction

     Background: The residential and commercial buildings sector accounted for 20% of global energy consumption in 2018 [1], and a much higher 39% in the United States in 2019 [2]. Many research efforts are focused on reducing the energy consumption, increasing the energy efficiency of buildings, and reducing carbon emission. Buildings can also provide services to utilities to enable deeper penetration of renewable energy on the grid. One of the major pathways to achieve these goals is to first understand the operational performance of buildings with collected data. The performance reflected in these data either informs the reality of existing buildings, reflects the effects of short- or long-term events and improvements in buildings, or aids the development of innovative approaches for maximizing building energy efficiency. The focus of this literature review aligns with the last use case, where extracting insights from the data greatly benefits further research.

     Definition of privacy: The big data era saw a massive increase in data collection as well as unprecedented privacy threats. It is worth noting what privacy means in this field of study because the definition of privacy has evolved over the years. An early definition of privacy started with “the right to be left alone” [3]; however, social situations have evolved since then, as mentioned by Hayashi [4] who notes “self-determination such as sexual orientation or contraception is admitted as one of the privacy elements.” Similar evolution also occurred and continues to occur in data collected from buildings. Granular data, such as smart meter data collected from advanced metering infrastructure (AMI) in sub-hourly (e.g., down to 15 min) intervals, has become available (owned by many utility companies around the world), but research has also shown that these data include various types of private information given the strong correlation of occupants (or building operators) with building energy consumption. Thus, there is a need to define privacy, especially for data collected in buildings. While Warren and Brandeis [3], Prosser [5], and Clark [6] defined privacy in general, Hayashi [4], Begum and Nausheen [7], and Jain et al. [8] considered privacy in terms of big data, and Pillitteri and Brewer [9] specifically focused on the context of smart grids. Pillitteri and Brewer [9] translated the privacy classifications defined by Clark [6] in the context of the smart grid application as shown in Table 1.This literature review follows the definition of privacy depicted by Pillitteri and Brewer [9].

Conclusions

     This article reviewed previous literature to provide building researchers with the basics of differential privacy implementation around data collected in buildings. Although the specific scope of this article is still in an early research stage, fast-growing data collection in buildings along with the breadth and scale of data available necessitated an examination of privacy preserving research for differential privacy. Because the topic is also not common in the representative building research journals, this study aimed to provide a greater level of detail for readers who might never have heard of differential privacy. To provide relevant context around privacy preservation in data, this literature review presented 1) privacy risks associated with data collected in buildings, 2) use cases that could be supported by analyses from these data, and 3) reviews of differential privacy implementation. The findings from the literature emphasize not only technical development but also engagement from stakeholders and policymakers in properly configuring differential privacy and protecting underlying privacy in data collected in buildings.

Abstract

     In recent years, deep learning (DL) has been ubiquitous in several areas, such as text recognition and data analysis, limited by this and increasingly used in security and data protection applications. Thus, the DL method has achieved remarkable big data analysis growth to avoid different attacks. This paper presents different methods for protecting privacy for DL in big data analysis. First, some possible attacks are explained, and then some basic approaches to protecting privacy in big data platforms are explained. In each section, drawbacks of the corresponding attacks are elaborated, and DL-based methods’ effectiveness in privacy preservation has been discussed. Finally, an effective solution for enhancing privacy preservation in DL models is given. The several DL-based privacy preservation methods for big data analysis and their advantages and disadvantages are elaborated. At last, drawbacks of DL based methods are highlighted, and future scope is given to address these issues.

Introduction

     In the era of digital technology, incredible amounts of data are produced by multiple organizations such as social media, banks, end sources and hospitals, etc. Social media generates tons of data every day, leading to huge data.1 Big data refer to the data collected from various sources such as machines, people, and things. The human-derived data can be individuals, text generation, and videos uploaded to the Internet. Machines generate various files, multimedia, and audits, while the data collected by multiple digital sensor devices are called data of things.2,3 Characteristics of big data are expressed by 5 V; large volume, higher velocity, greater variety, high value, and lower veracity. Volume denotes the speed of data collected, while the type of collected data is called diversity.4 Technological advances in healthcare make it easier to collect patient data electronically, which is presented as big data.5 In the modern medical system, patients are treated with multiple medical records. Thus, it is necessary to confirm secure data exchange to facilitate patient treatment in multiple hospitals.6

Conclusion

     The technological advancements used the digital platform for data communication simultaneously, and data privacy is a prime concern. The data associated with several fields must be processed securely with information leakage. For analyzing a large amount of data, the DL methods are adopted. At the same time, several attacks related to these methods damage the data communication, especially in the case of the big data platform. Thus, DL privacy preservation has become an important research area due to the privacy concern of a large amount of private data. If an attacker accesses personal information, it will cause data loss to users. Moreover, the information leakage in DL is happened due to internal and external factors. Thus, an effective approach toward privacy protection schemes greatly influences the enhancement of privacy preservation in DL. Several privacy preservation methods are reviewed in this work and their pros and cons. The methods reviewed in this paper are classified based on anonymization, optimizationbased approaches, and cryptographic methods. Moreover, several possible attacks related to DL were recalled. We can conclude from these papers that DL-based methods are more effective than other classic methods.