Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+ to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called Albatross. The system protects location information from the service provider and yet enables fine-grained location-sharing. One main feature of the system is to protect an individual's social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. Albatross protects locations sharing preferences through protocol unification and masking. Albatross has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.
Data sharing can be granted using different factors one of which is something in a users or an IoT devices environment which is in this paper broadcast signals. Using broadcast signals to measure Received Signal Strength Indicator values and Machine Learning models this paper implements an IoT data sharing scheme based on something that is in an IoT devices environment. The proposed scheme is experimentally tested using different ML models and shows 97.78 percent as its highest accuracy.
Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockcha…
Privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce ($ε, T$)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, elim…
Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the risk of a potential cyberattack - the requirements for threat information sharing range from simplistic sharing of documents to threat intelligence sharing. Therefore, the storage and sharing of highly sensitive threat information raises considerable concerns regarding constructing a secure, trusted threat information exchange infrastructure. Estab…
As data collection and analysis become critical functions for many cloud applications, proper data sharing with approved parties is required. However, the traditional data sharing scheme through centralized data escrow servers may sacrifice owners' privacy and is weak in security. Mainly, the servers physically own all data while the original data owners have only virtual ownership and lose actual access control. Therefore, we propose a 3-layer SSE-ABE-AES (3LSAA) cryptography-based privacy-protected data-sharing protocol based on the assumption that servers are honest-but-curious. The 3LSAA p…
The recent spades of cyber security attacks have compromised end users' data safety and privacy in Medical Cyber-Physical Systems (MCPS). Traditional standard encryption algorithms for data protection are designed based on a viewpoint of system architecture rather than a viewpoint of end users. As such encryption algorithms are transferring the protection on the data to the protection on the keys, data safety and privacy will be compromised once the key is exposed. In this paper, we propose a secure data storage and sharing method consisted by a selective encryption algorithm combined with fra…
In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications usually is sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with error tolerance in smart grid, which ensures that the…
The emergence of cloud computing provides a new computing paradigm for users -- massive and complex computing tasks can be outsourced to cloud servers. However, the privacy issues also follow. Fully homomorphic encryption shows great potential in privacy-preserving computation, yet it is not ready for practice. At present, secure multiparty computation (MPC) remains mainly approach to deal with sensitive data. In this paper, following the secret sharing based MPC paradigm, we propose a secure 2-party computation scheme, in which cloud servers can securely evaluate functions with high efficienc…
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…
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.
Lack of trust between organisations and privacy concerns about their data are impediments to an otherwise potentially symbiotic joint data analysis. We propose DataRing, a data sharing system that allows mutually mistrusting participants to query each others' datasets in a privacy-preserving manner while ensuring the correctness of input datasets and query answers even in the presence of (cheating) participants deviating from their true datasets. By relying on the assumption that if only a small subset of rows of the true dataset are known, participants cannot submit answers to queries deviati…
Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists o…
Neural networks, with the capability to provide efficient predictive models, have been widely used in medical, financial, and other fields, bringing great convenience to our lives. However, the high accuracy of the model requires a large amount of data from multiple parties, raising public concerns about privacy. Privacy-preserving neural network based on multi-party computation is one of the current methods used to provide model training and inference under the premise of solving data privacy. In this study, we propose a new two-party privacy-preserving neural network training and inference f…
Privacy-Enhancing Encryption in Data Sharing: A Survey on Security, Performance and Functionality
Tianjin University（天津大学）；Southern Cross University（南十字星大学）；Zhongguancun Laboratory（中关村实验室）；City University of Hong Kong（香港城市大学）；Griffith University（格里菲斯大学）；Singapore Management University（新加坡管理大学）；University of Wollongong（伍伦贡大学）
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…
Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the data perturbation processes of DP and how DP noise protects their sensitive personal information. Consequently, they distrusted the techniques and chose to opt out of participating. In this project, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals' privacy and preserve group utility. Following pilot surveys and in…
Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or disclosure remains a key concern during the sharing service development process. While significant efforts have been undertaken to address various privacy issues in recent years, there is a surprising lack of review for privacy concerns in the cyber-enabled sharing world. To bridge the gap, in this study, we survey and evaluate existing and emerging privacy issues relating to sh…
Ride-sharing allows multiple persons to share their trips together in one vehicle instead of using multiple vehicles. This can reduce the number of vehicles in the street, which consequently can reduce air pollution, traffic congestion and transportation cost. However, a ride-sharing organization requires passengers to report sensitive location information about their trips to a trip organizing server (TOS) which creates a serious privacy issue. In addition, existing ride-sharing schemes are non-flexible, i.e., they require a driver and a rider to have exactly the same trip to share a ride. Mo…
Data Sharing with Endogenous Choices over Differential Privacy Levels
The Ohio State University（俄亥俄州立大学）；MIT（麻省理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google（谷歌）；Georgia Institute of Technology（佐治亚理工学院）
With the development of electric vehicles, more and more electric vehicles have difficulties in parking and charging. One of the reasons is that the number of charging piles is difficult to support the energy supply of electric vehicles, and a large number of private charging piles have a long idle time, so the energy supply problem of electric vehicles can be solved by sharing charging piles. The shared charging pile scheme uses Paillier encryption scheme and improved scheme to effectively protect user data. The scheme has homomorphism of addition and subtraction, and can process information …
Privacy-Preserving and Secure Spectrum Sharing for Database-Driven Cognitive Radio Networks
University of South Florida（南佛罗里达大学）；Bellini College of Artificial Intelligence, Cybersecurity and Computing（贝利尼人工智能、网络安全与计算学院）；Zurich University of Applied Sciences (ZHAW)（苏黎世应用科学大学）；Department of Informatics, University of Zurich (UZH)（苏黎世大学信息学系）
The rapid growth of digital images motivates individuals and organizations to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would strongly limit the efficient usage of images. Plenty of existing schemes on privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the balance between security and retrieval ability. However, compared to the advanced technologies in CBIR like Convolutional Neural Network (CNN), the existing PPCBIR schemes are far deficient in both accuracy and efficiency. With mor…
Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general fra…
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation…
Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is suscept…
A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and interpretation of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. In this study, we use three common but distinct data types collected …
Systematic Survey on Privacy-Preserving Architectures for IoT and Vehicular Data Sharing: Techniques, Challenges, and Future Directions
Ho Chi Minh City University of Technology (HCMUT)（胡志明市理工大学）；Vietnam National University Ho Chi Minh City（越南国立大学胡志明市）；FPT University（FPT大学）；RMIT University（皇家墨尔本理工大学）；Ton Duc Thang University（顿特堂大学）
Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with untrusted third parties. In this work, we study privacy-utility trade-off in location sharing mechanisms. Existing approaches are mainly focused on privacy of sharing a single location or myopic location trace privacy; neither of them taking into account the temporal correlations between the past and current locations. Although these methods preserve the privacy for the current time, they may leak significant amount of …
Los Andes University（洛斯安第斯大学）；Rappi（Rappi公司）；Amazon（亚马逊）
Privacy-preserving machine learning in data-sharing processes is an ever-critical task that enables collaborative training of Machine Learning (ML) models without the need to share the original data sources. It is especially relevant when an organization must assure that sensitive data remains private throughout the whole ML pipeline, i.e., training and inference phases. This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacy-preserving embedded data. Thus, organizations can share the data representation to increase machine learning mo…
Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However, there is a major concern regarding data privacy on these cloud-based platforms. This work presents novel cloud-based privacy-preserving solutions to support collaborative consumption applications for sharing economy. In typical collaborative consumption, information processing platforms need to enable fair cost-sharing among multiple users for utilizing certain shared facilities and communal services. Our cloud-based…
Blockchain technologies have been boosting the development of data-driven decentralized services in a wide range of fields. However, with the spirit of full transparency, many public blockchains expose all types of data to the public such as Ethereum. Besides, the on-chain persistence of large data is significantly expensive technically and economically. These issues lead to the difficulty of sharing fairly large private data while preserving attractive properties of public blockchains. Although direct encryption for on-chain data persistence can introduce confidentiality, new challenges such …
