Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions.
Physical health records belong to healthcare providers, but the information contained within belongs to each patient. In an increasing manner, more health-related data is being acquired by wearables and other IoT devices following the ever-increasing trend of the "Quantified Self". Even though data protection regulations (e.g., GDPR) encourage the usage of privacy-preserving processing techniques, most of the current IoT infrastructure was not originally conceived for such purposes. One of the most used communication protocols, MQTT, is a lightweight publish-subscribe protocol commonly used in…
People and machines are collecting data at an unprecedented rate. Despite this newfound abundance of data, progress has been slow in sharing it for open science, business, and other data-intensive endeavors. Many such efforts are stymied by privacy concerns and regulatory compliance issues. For example, many hospitals are interested in pooling their medical records for research, but none may disclose arbitrary patient records to researchers or other healthcare providers. In this context we propose the Private Data Network (PDN), a federated database for querying over the collective data of mut…
The paper describes several original cryptographic cipher modules (VSEM) that are based on using one time pseudorandom pad and pseudorandom transpositions. The VSEM includes 4 modules of encryption that can be applied in combinations. We studied ability of these modules to secure the private data against attacks and their speed of encryption. The VSEM encryption was implemented in Fendoff applications for mobile devices on iOS and Android platforms as well as in computer application running Window or Mac OS. We describe these applications designed to encrypt/decrypt various personal data such …
This paper discusses one of the most significant challenges of next-generation big data (BD) federation platforms, namely, Hadoop access control. Privacy and security on a federation scale remain significant concerns among practitioners. Hadoop's current primitive access control presents security concerns and limitations, such as the complexity of deployment and the consumption of resources. However, this major concern has not been a subject of intensive study in the literature. This paper critically reviews and investigates these security limitations and provides a framework called BD federat…
Very recently, we are witnessing the emergence of a number of start-ups that enables individuals to sell their private data directly to brokers and businesses. While this new paradigm may shift the balance of power between individuals and companies that harvest data, it raises some practical, fundamental questions for users of these services: how they should decide which data must be vended and which data protected, and what a good deal is. In this work, we investigate a mechanism that aims at helping users address these questions. The investigated mechanism relies on a hard-privacy model and …
Securely sharing and managing personal content is a challenging task in multi-device environments. In this paper, we design and implement a new platform called Personal Content Networking (PCN). Our work is inspired by Content-Centric Networking (CCN) because we aim to enable access to personal content using its name instead of its location. The unique challenge of PCN is to support secure file operations such as replication, updates, and access control over distributed untrusted devices. The primary contribution of this work is the design and implementation of a secure content management plat…
While personal cloud storage services such as Dropbox, OneDrive, Google Drive and iCloud have become very popular in recent years, these services offer few security guarantees to users. These cloud services are aimed at end users, whose applications often assume a local file system storage, and thus require strongly consistent data. In addition, users usually access these services using personal computers and portable devices such as phones and tablets, which are upload bandwidth constrained and in many cases battery powered. Unity is a system that provides confidentiality, integrity, durabili…
We propose an agile softwarized infrastructure for flexible, cost effective, secure and privacy preserving deployment of Internet of Things (IoT) for smart healthcare applications and services. It integrates state-of-the-art networking and virtualization techniques across IoT, fog and cloud domains, employing Blockchain, Tor and message brokers to provide security and privacy for patients and healthcare providers. We propose a novel platform using Machine-to-Machine (M2M) messaging and rule-based beacons for seamless data management and discuss the role of data and decision fusion in the cloud…
The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions' repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the burden of conventional cloud computing by availing intermediate fog nodes between the end users …
Over the past few years, online service providers have started gathering increasing amounts of personal information to build user profiles and monetize them with advertisers and data brokers. Users have little control of what information is processed and are often left with an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model -- the "gist" -- of their data. We aim to preserve data utility and simultaneously provide user privacy. We show that this approach can be efficientl…
The increasing popular interest in personal telemetry, also called the Quantified Self or "lifelogging", has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative…
School of Engineering and Technology, University of Washington Tacoma（华盛顿大学塔科马分校工程与技术学院）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；Ghent University（根特大学）
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the applic…
A growing framework of legal and ethical requirements limit scientific and commercial evalua-tion of personal data. Typically, pseudonymization, encryption, or methods of distributed com-puting try to protect individual privacy. However, computational infrastructures still depend on human system administrators. This introduces severe security risks and has strong impact on privacy: system administrators have unlimited access to the computers that they manage in-cluding encryption keys and pseudonymization-tables. Distributed computing and data obfuscation technologies reduce but do not elimina…
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on third parties for storing and managing data. However, third parties are often not trusted to store plaintext personal and sensitive data; data encryption is widely adopted to protect against intentional and unintentional attempts to read personal/sensitive data. Traditional encryption schemes do not support operations over the ciphertexts and thus anonymizing…
Hong Kong University of Science and Technology（香港科技大学）；Clustar；Peking University（北京大学）；Fuzhou University（福州大学）
Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfuscation methods to protect the original ratings from leakage. However, the former comes with extra communication and computation costs, and the latter damages model accuracy. Neither of them could simultaneously satisfy the real-time feedback and accurate personalization requirements of recommender systems. In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federa…
In a Public Safety (PS) situation, agents may require critical and personally identifiable information. Therefore, not only does context and location-aware information need to be available, but also the privacy of such information should be preserved. Existing solutions do not address such a problem in a PS environment. This paper proposes a framework in which anonymized Personal Information (PI) is accessible to authorized public safety agents under a PS circumstance. In particular, we propose a secure data storage structure along with privacy-preserving mobile search framework, suitable for …
We are tackling the problem of trading real-world private information using only cryptographic protocols and a public blockchain to guarantee honest transactions. In this project, we consider three types of agents --buyers, sellers and notaries-- interacting in a decentralized privacy-preserving data marketplace (dPDM) such as the Wibson data marketplace. This framework offers infrastructure and financial incentives for individuals to securely sell personal information while preserving personal privacy. Here we provide an efficient cryptographic primitive for the secure exchange of data in a d…
Wibson is a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell information in a trusted environment. The combination of the Wibson token and blockchain-enabled smart contracts hopes to allow Data Sellers and Data Buyers to transact with each other directly while providing individuals the ability to maintain anonymity as desired. The Wibson marketplace will provide infrastructure and financial incentives for individuals to securely sell personal information without sacrificing personal privacy. Data Buyers receive information from w…
There are a lot of on going efforts in the research community as well as industry around providing privacy-preserving and secure storage for personal data. Although, over time it has adopted many tag lines such as Personal Information Hub [12], personal container [8], DataBox [4], Personal Data Store (PDS) [3] and many others, these are essentially reincarnations of a simple idea: provide a secure way and place for users to store their information and allow them to provision who has access to that information. In this paper, we would like to discuss a way to facilitate access control mechanism…
XML Signature Wrapping (XSW) has been a relevant threat to web services for 15 years until today. Using the Personal Health Record (PHR), which is currently under development in Germany, we investigate a current SOAP-based web services system as a case study. In doing so, we highlight several deficiencies in defending against XSW. Using this real-world contemporary example as motivation, we introduce a guideline for more secure XML signature processing that provides practitioners with easier access to the effective countermeasures identified in the current state of research.
Software privacy provides the ability to limit data access to unauthorized parties. Privacy is achieved through different means, such as implementing GDPR into software applications. However, previous research revealed that the lack of poor coding behaviour leads to privacy breaches such as personal data breaching. Therefore, this research proposes a novel game framework as a training intervention enabling developers to implement privacy-preserving software systems. The proposed framework was empirically investigated through a survey study (with some exit questions), which revealed the element…
The unique properties of blockchain enable central requirements of distributed secure logging: Immutability, integrity, and availability. Especially when providing transparency about data usages, a blockchain-based secure log can be beneficial, as no trusted third party is required. Yet, with data governed by privacy legislation such as the GDPR or CCPA, the core advantage of immutability becomes a liability. After a rightful request, an individual's personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve this issue, we exploit a legal property o…
University Hospital Tübingen（图宾根大学医院）；University of Tübingen（图宾根大学）
The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an 'algorithm to the data' paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-…
Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these issues, this paper proposes a healthcare information security storage solution based on Hyperledger Fabric and the Attribute-Based Access Control (ABAC) framework. The scheme first utilizes attribute-based access control, which allows dynamic…
Health data is a sensitive category of personal data. It might result in a high risk to individual and health information handling rights and opportunities unless there is a palatable defense. Reasonable security standards are needed to protect electronic health records (EHR). All personal data handling needs adequate explanation. Maintaining access to medical data even in the developing world would favor health and well-being across the world. Unfortunately, there are still countries that hinder the portability of medical records. Numerous occurrences have shown that it still takes weeks for …
Privacy is a major concern in designing any cryptographic primitive when frequent transactions are done electronically. During electronic transactions, people reveal their personal data into several servers and believe that this information does not leak too much about them. The adaptive oblivious transfer with hidden access policy (AOT-HAP) takes measure against such privacy issues. The existing AOT-HAP involves a sender and multiple receivers apart from a designated issuer. Security of these schemes rely on the fact that the issuer cannot collude with a set of receivers. Moreover, they loose…
Our aim is for Wibson to be a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell information in a trusted environment. The combination of the Wibson token and blockchain-enabled smart contracts hopes to allow Data Sellers and Data Buyers to transact with each other directly while providing individuals the ability to maintain anonymity as desired. Wibson intends that its data marketplace will provide infrastructure and financial incentives for individuals to securely sell personal information without sacrificing personal privacy. Da…
The passage of FISMA and HIPPA Acts have mandated various security controls that ensure the privacy of patients data. Hospitals and health-care organizations are required by law to ensure that patients data is stored and disseminated in a secure fashion. The advent of Bring Your Own Devices (BYOD), mobile devices, instant messaging (such as WhatsApp) and cloud technology however, have brought forth new challenges. The advent of Internet of Things (IOT) have complicated the matters further as organizations are not fully cognizant to the all facets of threats to data privacy. Physicians and heal…
Online social networks are used frequently by many people: Staying in contact with friends and sharing experiences with them is very important. However, users are increasingly concerned that their data will end up in the hands of strangers or that personal data may even be misused. Secure OSNs can help. These often use different types of encryption to keep the communication between the participants incomprehensible to outsiders. However, participants in such social networks cannot be sure that their data is secure. Various approaches show that even harmless-looking metadata, such as the number…
Personal cryptographic keys are the foundation of many secure services, but storing these keys securely is a challenge, especially if they are used from multiple devices. Storing keys in a centralized location, like an Internet-accessible server, raises serious security concerns (e.g. server compromise). Hardware-based Trusted Execution Environments (TEEs) are a well-known solution for protecting sensitive data in untrusted environments, and are now becoming available on commodity server platforms. Although the idea of protecting keys using a server-side TEE is straight-forward, in this paper …
With the advent of cloud technologies, there is a growing number of easy-to-use services to store files and share them with other cloud users. By providing security features, cloud service providers try to encourage users to store personal files or corporate documents on their servers. However, their server-side encryption solutions are not satisfactory when the server itself is not trusted. Although, there are several client-side solutions to provide security for cloud sharing, they are not used extensively because of usability issues in key management. In this paper, we propose TwinCloud whi…
Using communication services is a common part of everyday life in a personal or business context. Communication services include Internet services like voice services, chat service, and web 2.0 technologies (wikis, blogs, etc), but other usage areas like home energy management and eMobility are will be increasingly tackled. Such communication services typically authenticate participants. For this identities of some kind are used to identify the communication peer to the user of a service or to the service itself. Calling line identification used in the Session Initiation Protocol (SIP) used fo…
Recent advances in DNA sequencing technologies have put ubiquitous availability of fully sequenced human genomes within reach. It is no longer hard to imagine the day when everyone will have the means to obtain and store one's own DNA sequence. Widespread and affordable availability of fully sequenced genomes immediately opens up important opportunities in a number of health-related fields. In particular, common genomic applications and tests performed in vitro today will soon be conducted computationally, using digitized genomes. New applications will be developed as genome-enabled medicine b…
Data security and personal privacy are difficult to maintain in the Internet age. In 2012, professional networking site LinkedIn suffered a breach, compromising the login of over 100 million accounts. The passwords were cracked and sold online, exposing the authentication credentials millions of users. This manuscript dissects the cryptographic failures implicated in the breach, and explores more secure methods of storing passwords.
The pervasive adoption of Internet-connected digital services has led to a growing concern in the personal data privacy of their customers. On the other hand, machine learning (ML) techniques have been widely adopted by digital service providers to improve operational productivity and customer satisfaction. ML inevitably accesses and processes users' personal data, which could potentially breach the relevant privacy protection regulations if not performed carefully. The situation is exacerbated by the cloud-based implementation of digital services when user data are captured and stored in dist…
The paper reviews an implementation of an additional encrypted tunnel within TLS to further secure and authenticate the traffic of personal information between ProtonMail's frontends and the backend, implementing its key exchange, symmetric packet encryption, and validation. Technologies such as Secure Remote Password (SRP) and the Elliptic Curves Diffie Hellman Ephemeral (ECDHE) exchange are used for the key exchange, verifying the public parameters through PGP signatures. The data is then transferred encrypted with AES-128-GCM. This project is meant to integrate TLS security for high securit…
Secure On-Device Video OOD Detection Without Backpropagation
University of Southern California（南加州大学）；National University of Singapore（新加坡国立大学）；University of California, Berkeley（加州大学伯克利分校）；University of Florida（佛罗里达大学）；Texas A&M University（德克萨斯农工大学）；Arizona State University（亚利桑那州立大学）
Every time the customer (individual or company) has to release personal information to its service provider (e.g., an online store or a cloud computing provider), it faces a trade-off between the benefits gained (enhanced or cheaper services) and the risks it incurs (identity theft and fraudulent uses). The amount of personal information released is the major decision variable in that trade-off problem, and has a proxy in the maximum loss the customer may incur. We find the conditions for a unique optimal solution to exist for that problem as that maximizing the customer's surplus. We also sho…
This paper reports on the development and evaluation of a prototype blockchain solution for private and secure individual omics health data management and sharing. This solution is one output of a multidisciplinary project investigating the social, data and technical issues surrounding application of blockchain technology in the context of personalized healthcare research. The project studies potential ethical, legal, social and cognitive constraints of self-sovereign healthcare data management and sharing, and whether such constraints can be addressed through careful user interface design of …
Privacy of the outsourced data is one of the major challenge.Insecurity of the network environment and untrustworthiness of the service providers are obstacles of making the database as a service.Collection and storage of personally identifiable information is a major privacy concern.On-line public databases and resources pose a significant risk to user privacy, since a malicious database owner may monitor user queries and infer useful information about the customer.The challenge in data privacy is to share data with third-party and at the same time securing the valuable information from unaut…
There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea's approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our sp…
Hong Kong University of Science and Technology（香港科技大学）；Clustar；Peking University（北京大学）；WeBank（微众银行）
To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data. In this paper, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gra…
