Privacy-enhancing Technologies Research Articles

Monitoring an anonymity network: Toward the deanonymization of hidden services

Anonymity networks are an example of Privacy Enhancing Technology (PET) whose historical goal is to avoid censorship, preserve users privacy, and promote freedom of speech. Such networks, however, also provide a “safe haven” for criminal activity: previous research observed a dominance of commerce platforms delivered as hidden services within The Onion Router (Tor) network, undoubtedly the most popular anonymization technology at the time of writing, largely around narcotics and illegal financial services.Extensive research has been conducted on locating hidden services on the Tor network, but a general method that is able, given a service delivered via anonymity network, to effectively produce a list of candidate nodes responsible for delivering the service still remains an open research problem. In this paper we describe the infrastructure we have designed and implemented for monitoring the Invisible Internet Project (I2P) network, which is a smaller scale anonymity network compared to Tor but already proven to be used for illicit activities, and how its output can be used to enable such general method.

Machine Learning-Based Analysis of Encrypted Medical Data in the Cloud: Qualitative Study of Expert Stakeholders' Perspectives.

BackgroundThird-party cloud-based data analysis applications are proliferating in electronic health (eHealth) because of the expertise offered and their monetary advantage. However, privacy and security are critical concerns when handling sensitive medical data in the cloud. Technical advances based on “crypto magic” in privacy-preserving machine learning (ML) enable data analysis in encrypted form for maintaining confidentiality. Such privacy-enhancing technologies (PETs) could be counterintuitive to relevant stakeholders in eHealth, which could in turn hinder adoption; thus, more attention is needed on human factors for establishing trust and transparency.ObjectiveThe aim of this study was to analyze eHealth expert stakeholders’ perspectives and the perceived tradeoffs in regard to data analysis on encrypted medical data in the cloud, and to derive user requirements for development of a privacy-preserving data analysis tool.MethodsWe used semistructured interviews and report on 14 interviews with individuals having medical, technical, or research expertise in eHealth. We used thematic analysis for analyzing interview data. In addition, we conducted a workshop for eliciting requirements.ResultsOur results show differences in the understanding of and in trusting the technology; caution is advised by technical experts, whereas patient safety assurances are required by medical experts. Themes were identified with general perspectives on data privacy and practices (eg, acceptance of using external services), as well as themes highlighting specific perspectives (eg, data protection drawbacks and concerns of the data analysis on encrypted data). The latter themes result in requiring assurances and conformance testing for trusting tools such as the proposed ML-based tool. Communicating privacy, and utility benefits and tradeoffs with stakeholders is essential for trust. Furthermore, stakeholders and their organizations share accountability of patient data. Finally, stakeholders stressed the importance of informing patients about the privacy of their data.ConclusionsUnderstanding the benefits and risks of using eHealth PETs is crucial, and collaboration among diverse stakeholders is essential. Assurances of the tool’s privacy, accuracy, and patient safety should be in place for establishing trust of ML-based PETs, especially if used in the cloud.

Introduction: digital cultures and acts of refusal, secrecy and power across privacy-enhancing technologies

The Dark Social: Online Practices of Resistance, Motility and Power explores the way that users engage online by managing the capacity for anonymous communication in encrypted, hidden, or otherwise...

SpearSim: Design and Evaluation of Synthetic Task Environment for Studies on Spear Phishing Attacks

Despite significant advancements in security technologies, phishing attacks continue to be rampant and successful because distinguishing phishing emails from real messages remains difficult to most end-users, mainly the targeted kind known as spear-phishing. There is a severe lack of human factor studies on spear-phishing attacks due to lack of methods and datasets. We have designed a novel multi-player synthetic task environment, called SpearSim, for conducting laboratory experiments on spear-phishing attacks. Using SpearSim, we have conducted an experiment to understand how information exploitation in spear-phishing attacks influences end-user decision-making. This paper describes the SpearSim system’s design and discusses the results from the experiment conducted with SpearSim. The experiment results show that people are more vulnerable to spear-phishing attacks when attackers can explore and exploit different kinds of personal information available to them about their targets. We discuss the implications of this research for the design of anti-phishing training solutions and privacy enhancing technologies.

Secure Channel for Sharing Datasets by using Privacy-Preserving Integration Method

In privacy-enhancing technology, it has been inevitably challenging to strike a maintain balance between privacy, efficiency and usability (utility). We propose a highly practical and efficient approach for privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, Secure Channel. Through Secure Channel, each participant is able to randomize their datasets via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants group in a privacy-preserving manner. Our process does not require any public or key sharing between participants in order to integrate different datasets. This, in turn, leads to a user can understand and use easily and scalable solution. Moreover, the accuracy of a randomized dataset which are returned by the third party can be securely verified by the other participant of group. We further demonstrate Secure Channel’s general utilities, using it to construct a structure preserving data integration protocol. This is mainly useful for, good quality integration of network traffic data.

Speed Reading in the Dark: Accelerating Functional Encryption for Quadratic Functions with Reprogrammable Hardware

Functional encryption is a new paradigm for encryption where decryption does not give the entire plaintext but only some function of it. Functional encryption has great potential in privacy-enhancing technologies but suffers from excessive computational overheads. We introduce the first hardware accelerator that supports functional encryption for quadratic functions. Our accelerator is implemented on a reprogrammable system-on-chip following the hardware/software codesign methogology. We benchmark our implementation for two privacy-preserving machine learning applications: (1) classification of handwritten digits from the MNIST database and (2) classification of clothes images from the Fashion MNIST database. In both cases, classification is performed with encrypted images. We show that our implementation offers speedups of over 200 times compared to a published software implementation and permits applications which are unfeasible with software-only solutions.

A Digital Currency Architecture for Privacy and Owner-Custodianship

In recent years, electronic retail payment mechanisms, especially e-commerce and card payments at the point of sale, have increasingly replaced cash in many developed countries. As a result, societies are losing a critical public retail payment option, and retail consumers are losing important rights associated with using cash. To address this concern, we propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both e-commerce purchases and point-of-sale purchases that are required to be cashless. Our proposal introduces a government-backed, privately-operated digital currency infrastructure to ensure that every transaction is registered by a bank or money services business, and it relies upon non-custodial wallets backed by privacy-enhancing technology, such as blind signatures or zero-knowledge proofs, to ensure that transaction counterparties are not revealed. Our approach to digital currency can also facilitate more efficient and transparent clearing, settlement, and management of systemic risk. We argue that our system can restore and preserve the salient features of cash, including privacy, owner-custodianship, fungibility, and accessibility, while also preserving fractional reserve banking and the existing two-tiered banking system. We also show that it is possible to introduce regulation of digital currency transactions involving non-custodial wallets that unconditionally protect the privacy of end-users.

Wastewater Monitoring Raises Privacy and Ethical Considerations

Analysis of municipal wastewater, or sewage for public health applications is a rapidly expanding field aimed at understanding emerging epidemiological trends, including human and disease migration. The newly gained ability to extract and analyze genetic material from wastewater poses important societal and ethical questions, including: How to safeguard data? Who owns genetic data recovered from wastewater? and What are the ethical and legal issues surrounding its use? In the U.S., both corporate and legal policies regarding privacy have been historically reactive instead of proactive. In wastewater-based epidemiology (WBE), the pace of innovation has outpaced the ability of social and legal mechanisms to keep up. To address this discrepancy, early and robust discussions of the research, policies, and ethics surrounding WBE analysis and genetics are needed. This article contributes to this discussion by examining ownership issues for human genetic data recovered from wastewater and the uses to which it may be put. We focus particularly on the risks associated with personally identifiable data, highlighting potential risks, relevant privacy-enhancing technologies, and appropriate ethics. This article proposes an approach for people conducting WBE studies to help them systematically consider the ethical and privacy implications of their work.

Investigative Techniques for the De-Anonymization of Hidden Services

Anonymity networks, also referred to as anonymous networks or darknets are an example of privacy-enhancing technology, the historical goal of which is to avoid censorship, preserve user privacy, and promote freedom of speech. The Tor network, undoubtedly the most popular anonymization technology at the time of this writing, according to its website, has several categories of users: individuals who wish to safeguard their online activity, businesses wanting to keep data confidential or conducting competitive analysis, journalists willing to protect anonymous sources and sensitive research, and activists who want to report abuses from danger zones.

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis.

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 utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.

Post-Quantum Era Privacy Protection for Intelligent Infrastructures

As we move into a new decade, the global world of Intelligent Infrastructure (II) services integrated into the Internet of Things (IoT) are at the forefront of technological advancements. With billions of connected devices spanning continents through interconnected networks, security and privacy protection techniques for the emerging II services become a paramount concern. In this paper, an up-to-date privacy method mapping and relevant use cases are surveyed for II services. Particularly, we emphasize on post-quantum cryptography techniques that may (or must when quantum computers become a reality) be used in the future through concrete products, pilots, and projects. The topics presented in this paper are of utmost importance as (1) several recent regulations such as Europe’s General Data Protection Regulation (GDPR) have given privacy a significant place in digital society, and (2) the increase of IoT/II applications and digital services with growing data collection capabilities are introducing new threats and risks on citizens’ privacy. This in-depth survey begins with an overview of security and privacy threats in IoT/IIs. Next, we summarize some selected Privacy-Enhancing Technologies (PETs) suitable for privacy-concerned II services, and then map recent PET schemes based on post-quantum cryptographic primitives which are capable of withstanding quantum computing attacks. This paper also overviews how PETs can be deployed in practical use cases in the scope of IoT/IIs, and maps some current projects, pilots, and products that deal with PETs. A practical case study on the Internet of Vehicles (IoV) is presented to demonstrate how PETs can be applied in reality. Finally, we discuss the main challenges with respect to current PETs and highlight some future directions for developing their post-quantum counterparts.

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

Private data is transmitted and stored online every second. Therefore, security and privacy assurances should be provided at all times. However, that is not always the case. Private information is often unwillingly collected, sold, or exposed, depriving data owners of their rightful privacy. In this article, various privacy threats, concepts, regulations, and personal data types are analyzed. An overview of Privacy Enhancing Technologies (PETs) and a survey of anonymization mechanisms, privacy tools, models, and metrics are presented together with an analysis of respective characteristics and capabilities. Moreover, this article analyses the applicability of the reviewed privacy mechanisms on today's Cloud Services and identifies the current research challenges to achieve higher privacy levels in the Cloud.

A Digital Currency Architecture for Privacy and Owner-Custodianship

In recent years, electronic retail payment mechanisms, especially e-commerce and card payments at the point of sale, have increasingly replaced cash in many developed countries. As a result, societies are losing a critical public retail payment option, and retail consumers are losing important rights associated with using cash. To address this concern, we propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both internet purchases and point-of-sale purchases that are required to be cashless. Our proposal introduces a government-backed, privately-operated digital currency infrastructure to ensure that every transaction is registered by a bank or money services business, and it relies upon non-custodial wallets backed by privacy-enhancing technology such as blind signatures or zero-knowledge proofs to ensure that transaction counterparties are not revealed. Our approach to digital currency can also facilitate more efficient and transparent clearing, settlement, and management of systemic risk. We argue that our system can restore and preserve the salient features of cash, including privacy, owner custodianship, fungibility, and accessibility, while also preserving fractional reserve banking and the existing two-tiered banking system. We also show that it is possible to introduce regulation of digital currency transactions involving non-custodial wallets that unconditionally protect the privacy of end-users.

Privacy and Transparency in CBDCs: A Regulation-by-Design AML/CFT Scheme

Central banks and governments all over the world are increasingly exploring digital versions of fiat money, known as retail Central Bank Digital Currencies (CBDCs). Most initiatives rely on Distributed Ledger Technologies and are presented as alternatives to physical cash. Consequently, anonymity-related regulatory questions arise in terms of Anti-Money Laundering and Counter-Terrorist Financing compliance. Against this backdrop, this paper provides a techno-legal taxonomy of approaches to balance privacy and transparency in CBDCs without thwarting accountability, but it also underlines cross-sectoral impacts. The contribution heeds regulation-by-design as its core methodological foundation, with Privacy-Enhancing Technologies as the relevant use case. Thus, it highlights that not only technology aids legal purposes, but also that some regulatory requirements ought to be designed into technology for one to reach agreed upon results and/or standards.

AWA: Adversarial Website Adaptation

One of the most important obligations of privacy-enhancing technologies is to bring confidentiality and privacy to users’ browsing activities on the Internet. The website fingerprinting attack enables a local passive eavesdropper to predict the target user’s browsing activities even she uses anonymous technologies, such as VPNs, IPsec, and Tor. Recently, the growth of deep learning empowers adversaries to conduct the website fingerprinting attack with higher accuracy. In this paper, we propose a new defense against website fingerprinting attack using adversarial deep learning approaches called Adversarial Website Adaptation (AWA). AWA creates a transformer set in each run so that each website has a unique transformer. Each transformer generates adversarial traces to evade the adversary’s classifier. AWA has two versions, including Universal AWA (UAWA) and Non-Universal AWA (NUAWA). Unlike NUAWA, there is no need to access the entire trace of a website in order to generate an adversarial trace in UAWA. We accommodate secret random elements in the training phase of transformers in order for AWA to generate various sets of transformers in each run. We run AWA several times and create multiple sets of transformers. If an adversary and a target user select different sets of transformers, the accuracy of adversary’s classifier is almost 19.52% and 31.94% with almost 22.28% and 26.28% bandwidth overhead in UAWA and NUAWA, respectively. If a more powerful adversary generates adversarial traces through multiple sets of transformers and trains a classifier on them, the accuracy of adversary’s classifier is almost 49.10% and 25.93% with almost 62.52% and 64.33% bandwidth overhead in UAWA and NUAW, respectively.

The Cost of Progress: Exploring Privacy Nightmares for AI in Precision Medicine

Precision medicine is an innovative approach to healthcare that relies on the use of genomic data, electronic health records, and other types of medical data to develop personalized prevention, diagnosis, and treatment strategies for patients. The use of artificial intelligence (AI) in precision medicine has the potential to improve patient outcomes and reduce healthcare costs, but it also raises significant privacy concerns. This paper provides a comprehensive review of the privacy nightmares associated with the use of AI in precision medicine. We examine the potential risks and threats to patient privacy, including the use of personal data for unintended purposes, the risk of data breaches and hacking, and the potential for discrimination and bias. We also analyze the legal and ethical implications of using AI in precision medicine, including issues related to informed consent and data ownership. Our investigation highlights the need for strong data protection regulations and ethical frameworks to safeguard patient privacy in the age of AI in precision medicine. As the use of AI in precision medicine continues to expand, the paper presents a road for future directions for protecting patient privacy, including the use of privacy-preserving machine learning algorithms and the adoption of privacy-enhancing technologies.

Decentralized data processing: personal data stores and the GDPR

Online services are driven by data; functionality and value are derived from its processing. However, individuals generally have little visibility—let alone control—over what, how, why, and by whom their data are captured, analysed, transferred, stored, or otherwise used.1 In response to this, and to the growing public discourse regarding data-related issues, there is considerable focus by the computer science and engineering communities on developing privacy-enhancing technologies (PETs), ie technical tools and measures that can assist in addressing privacy concerns.2Personal data stores (PDSs) are one such technology, which aims to tackle issues of privacy and user control by providing an alternative, decentralized approach to data processing. The European Commission in its European strategy for data expressed that such tools have significant potential, and that a supportive environment to foster its development is necessary in order to realize its benefits.3 The current dominant data processing model involves the...

Enterprise-grade protection against e-mail tracking

E-mail tracking provides companies with fine-grained behavioral data about e-mail recipients, which can be a threat for individual privacy and enterprise security. This problem is especially severe since e-mail tracking techniques often gather data without the informed consent of the recipients. So far e-mail recipients lack a reliable protection mechanism.This article presents a novel protection framework against e-mail tracking that closes an important gap in the field of enterprise security and privacy-enhancing technologies. We conceptualize, implement and evaluate an anti-tracking mail server that is capable of identifying tracking images in e-mails via machine learning with very high accuracy, and can selectively replace them with arbitrary images containing warning messages for the recipient. Our mail protection framework implements a selective prevention strategy as enterprise-grade software using the design science research paradigm. It is flexibly extensible, highly scalable, and ready to be applied under actual production conditions. Experimental evaluations show that these goals are achieved through solid software design, adoption of recent technologies and the creation of novel flexible software components.

Privacy Enhancing Technologies and Synthetic Data

Privacy enhancing technologies (PETs) are a set of technologies that assist organizations in protecting data. In addition to PETs, there are Transparency Enhancing Technologies (TETs) and Intervenability Enhancing Technologies (IETs). These technologies are complementary approaches to PETs. TETs are “tools which can provide to the individual concerned clear visibility of aspects relevant to [its personal] data and the individual’s privacy” (Zimmerman, 2015). While PETs work to minimize data, TETs provide users with information regarding the data handling behavior of third parties. IETs provide users opportunities to intervene and encompass control including giving, denying or withdrawing consent, and blocking or erasing personal data. IETs “aim[] at the possibility for parties involved in any privacy-relevant data processing to interfere with the ongoing or planned data processing (Karegar, 2018).” However, technologies associated with intervenability are currently limited. This paper will provide an overview of PETs and then further explore the specific PET of synthetic data sets technology.

Hands off my data: users’ security concerns and intention to adopt privacy enhancing technologies

As the number of data leak scandals and data infringements increase by the day, people are becoming more concerned than ever about their online security. As a result, software and applications designed to reduce, eliminate, or prevent unauthorized processing of users’ online personal data, referred to as privacy enhancing technologies (PETs) are gaining momentum. Yet, research investigating what drives users’ intention to adopt these technologies is still scant. Drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study develops a research framework and tests it with a research design combining structural equation modelling and multi-group analysis. As participants, we recruited 198 members of four online communities where discussion of PETs takes place. Besides confirming the UTAUT2 variables’ predictive power on the intention to use PETs, the results of applying the baseline model also provide interesting insight on the mediating role UTAUT2 core constructs play in the relationship between security concerns and intention to adopt PETs. The multi-group analysis, in contrast, revealed that the underlying mechanisms of the theoretical framework we tested work differently when the users’ level of expertise is taken into account. This work concludes with managerial implications addressed to both PET providers and any business dealing with online consumer data.