Proceedings on Privacy Enhancing Technologies

The Tor anonymity network has been shown vulnerable to traffic analysis attacks by Autonomous Systems and Internet Exchanges who can observe different overlay hops belonging to the same circuit. We perform a case study to determine whether network path prediction techniques are suitable for avoiding such adversaries. We perform a measurement study by running traceroutes from Tor relays to destinations around the Internet. We use the data to evaluate the accuracy of the Autonomous Systems and Internet Exchanges that are predicted to appear on the path using state-of-the-art path inference techniques. We also consider to what extent overestimation can improve prediction accuracy.

Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. One of the latest such technologies, RAPPOR, allows the marginal frequencies of an arbitrary set of strings to be estimated via privacy-preserving crowdsourcing. However, this original estimation process requires a known set of possible strings; in practice, this dictionary can often be extremely large and sometimes completely unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of "unknown unknowns," i.e., strings we do not even know we should be estimating. To enable learning without explicit knowledge of the dictionary, we develop methodology for estimating the joint distribution of two or more variables collected with RAPPOR. This is a critical step towards understanding relationships between multiple variables collected in a privacy-preserving manner.

Confidential Content-Based Publish/Subscribe (C-CBPS) is an interaction (pub/sub) model that allows parties to exchange data while still protecting their security and privacy interests. In this paper we advance the state of the art in C-CBPS by showing how all predicate circuits in NC1 (logarithmic-depth, bounded fan-in) can be securely computed by a broker while guaranteeing perfect information-theoretic security. Previous work could handle only strictly shallower circuits (e.g. those with depth O(\sqrt{\lg n}) [SYY99, V76]. We present three protocols -- UGP-Match, FSGP-Match and OFSGP-Match -- all three are based on (2-decomposable randomized encodings of) group programs and handle circuits in NC1. UGP-Match is conceptually simple and has a clean proof of correctness but it is inefficient and impractical. FSGP-Match uses a "fixed structure" trick to achieve efficiency and scalability. And, finally, OFSGP-Match uses hand-optimized group programs to wring greater efficiencies. We complete our investigation with an experimental evaluation of a prototype implementation.

Consider users who share their data (e.g., location) with an untrusted service provider to obtain a personalized (e.g., location-based) service. Data obfuscation is a prevalent user-centric approach to protecting users' privacy in such systems: the untrusted entity only receives a noisy version of user's data. Perturbing data before sharing it, however, comes at the price of the users' utility (service quality) experience which is an inseparable design factor of obfuscation mechanisms. The entanglement of the utility loss and the privacy guarantee, in addition to the lack of a comprehensive notion of privacy, have led to the design of obfuscation mechanisms that are either suboptimal in terms of their utility loss, or ignore the user's information leakage in the past, or are limited to very specific notions of privacy which e.g., do not protect against adaptive inference attacks or the adversary with arbitrary background knowledge. In this paper, we design user-centric obfuscation mechanisms that impose the minimum utility loss for guaranteeing user's privacy. We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error). This double shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint differential-distortion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the differential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game between the designer of obfuscation mechanism and the potential adversary, and design adaptive mechanisms that anticipate and protect against optimal inference algorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm.

With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a “geographic fence” that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris’ wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.

Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. To illustrate this system, we present two novel types of adversaries. First, we study a powerful, pervasive adversary that can compromise an unknown number of Autonomous System organizations, Internet Exchange Point organizations, and Tor relay families. Second, we initiate the study of how an adversary might use Mutual Legal Assistance Treaties (MLATs) to enact surveillance. As part of this, we identify submarine cables as a potential subject of trust and incorporate data about these into our MLAT analysis by using them as a proxy for adversary power. Finally, we present preliminary experimental results that show the potential for our trust framework to be used by Tor clients and services to improve security.

People’s privacy sentiments influence changes in legislation as well as technology design and use. While single-point-in-time investigations of privacy sentiment offer useful insight, study of people’s privacy sentiments over time is also necessary to better understand and anticipate evolving privacy attitudes. In this work, we build off of a 2019 Pew Research study and use repeated cross-sectional surveys (n=6,676) from 2019, 2020, and 2021 to model the sentiments of people in the U.S. toward collection and use of data for government- and health-related purposes. After the onset of COVID-19, we observe significant decreases in respondent acceptance of government data use and significant increases in acceptance of health-related data uses. While differences in privacy attitudes between sociodemographic groups largely decreased over this time period, following the 2020 U.S. national elections, we observe some of the first evidence that privacy sentiments may change based on the alignment between a user’s politics and the political party in power. Our results offer insight into how privacy attitudes may have been impacted by recent events and allow us to identify potential predictors of changes in privacy attitudes during times of geopolitical or national change.

Secure Two-Party Computation (2PC) protocols allow two parties to compute a function of their private inputs without revealing any information besides the output of the computation. There exist low cost general-purpose protocols for semi-honest parties that can be efficiently executed even on smartphones. However, for the case of malicious parties, current 2PC protocols are significantly less efficient, limiting their use to more resourceful devices. In this work we present an efficient 2PC protocol that is secure against malicious parties and is light enough to be used on mobile phones. The protocol is an adaptation of the protocol of Nielsen et al. (Crypto, 2012) to the Server-Aided setting, a natural relaxation of the plain model for secure computation that allows the parties to interact with a server (e.g., a cloud) who is assumed not to collude with any of the parties. Our protocol has two stages: In an offline stage - where no party knows which function is to be computed, nor who else is participating - each party interacts with the server and downloads a file. Later, in the online stage, when two parties decide to execute a 2PC together, they can use the files they have downloaded earlier to execute the computation with cost that is lower than the currently best semi-honest 2PC protocols. We show an implementation of our protocol for Android mobile phones, discuss several optimizations and report on its evaluation for various circuits. For example, the online stage for evaluating a single AES circuit requires only 2.5 seconds and can be further reduced to 1 second (amortized time) with multiple executions.

Mobile communications are used by more than two-thirds of the world population who expect security and privacy guarantees. The 3rd Generation Partnership Project (3GPP) responsible for the worldwide standardization of mobile communication has designed and mandated the use of the AKA protocol to protect the subscribers’ mobile services. Even though privacy was a requirement, numerous subscriber location attacks have been demonstrated against AKA, some of which have been fixed or mitigated in the enhanced AKA protocol designed for 5G. In this paper, we reveal a new privacy attack against all variants of the AKA protocol, including 5G AKA, that breaches subscriber privacy more severely than known location privacy attacks do. Our attack exploits a new logical vulnerability we uncovered that would require dedicated fixes. We demonstrate the practical feasibility of our attack using low cost and widely available setups. Finally we conduct a security analysis of the vulnerability and discuss countermeasures to remedy our attack.

Proposed by the 3rd Generation Partnership Project (3GPP) as a standard for 3G and 4G mobile-network communications, the AKA protocol is meant to provide a mutually-authenticated key-exchange between clients and associated network servers. As a result AKA must guarantee the indistinguishability from random of the session keys (key-indistinguishability), as well as client- and server-impersonation resistance. A paramount requirement is also that of client privacy, which 3GPP defines in terms of: user identity confidentiality, service untraceability, and location untraceability. Moreover, since servers are sometimes untrusted (in the case of roaming), the AKA protocol must also protect clients with respect to these third parties. Following the description of client-tracking attacks e.g. by using error messages or IMSI catchers, van den Broek et al. and respectively Arapinis et al. each proposed a new variant of AKA, addressing such problems. In this paper we use the approach of provable security to show that these variants still fail to guarantee the privacy of mobile clients. We propose an improvement of AKA, which retains most of its structure and respects practical necessities such as key-management, but which provably attains security with respect to servers and Man-in-the- Middle (MiM) adversaries. Moreover, it is impossible to link client sessions in the absence of client-corruptions. Finally, we prove that any variant of AKA retaining its mutual authentication specificities cannot achieve client-unlinkability in the presence of corruptions. In this sense, our proposed variant is optimal.

Leveraging parallel hardware (e.g. GPUs) for deep neural network (DNN) training brings high computing performance. However, it raises data privacy concerns as GPUs lack a trusted environment to protect the data. Trusted execution environments (TEEs) have emerged as a promising solution to achieve privacypreserving learning. Unfortunately, TEEs’ limited computing power renders them not comparable to GPUs in performance. To improve the trade-off among privacy, computing performance, and model accuracy, we propose an asymmetric model decomposition framework, AsymML, to (1) accelerate training using parallel hardware; and (2) achieve a strong privacy guarantee using TEEs and differential privacy (DP) with much less accuracy compromised compared to DP-only methods. By exploiting the low-rank characteristics in training data and intermediate features, AsymML asymmetrically decomposes inputs and intermediate activations into low-rank and residual parts. With the decomposed data, the target DNN model is accordingly split into a trusted and an untrusted part. The trusted part performs computations on low-rank data, with low compute and memory costs. The untrusted part is fed with residuals perturbed by very small noise. Privacy, computing performance, and model accuracy are well managed by respectively delegating the trusted and the untrusted part to TEEs and GPUs. We provide a formal DP guarantee that demonstrates that, for the same privacy guarantee, combining asymmetric data decomposition and DP requires much smaller noise compared to solely using DP without decomposition. This improves the privacy-utility trade-off significantly compared to using only DP methods without decomposition. Furthermore, we present a rank bound analysis showing that the low-rank structure is preserved after each layer across the entire model. Our extensive evaluations on DNN models show that AsymML delivers 7.6× speedup in training compared to the TEE-only executions while ensuring privacy. We also demonstrate that AsymML is effective in protecting data under common attacks such as model inversion and gradient attacks.

This paper focuses on protecting the cellular paging protocol — which balances between the quality-of-service and battery consumption of a device — against security and privacy attacks. Attacks against this protocol can have severe repercussions, for instance, allowing attacker to infer a victim’s location, leak a victim’s IMSI, and inject fabricated emergency alerts. To secure the protocol, we first identify the underlying design weaknesses enabling such attacks and then propose efficient and backward-compatible approaches to address these weaknesses. We also demonstrate the deployment feasibility of our enhanced paging protocol by implementing it on an open-source cellular protocol library and commodity hardware. Our evaluation demonstrates that the enhanced protocol can thwart attacks without incurring substantial overhead.

Voice over LTE (VoLTE) and Voice over NR (VoNR), are two similar technologies that have been widely deployed by operators to provide a better calling experience in LTE and 5G networks, respectively. The VoLTE/NR protocols rely on the security features of the underlying LTE/5G network to protect users' privacy such that nobody can monitor calls and learn details about call times, duration, and direction. In this paper, we introduce a new privacy attack which enables adversaries to analyse encrypted LTE/5G traffic and recover any VoLTE/NR call details. We achieve this by implementing a novel mobile-relay adversary which is able to remain undetected by using an improved physical layer parameter guessing procedure. This adversary facilitates the recovery of encrypted configuration messages exchanged between victim devices and the mobile network. We further propose an identity mapping method which enables our mobile-relay adversary to link a victim's network identifiers to the phone number efficiently, requiring a single VoLTE protocol message. We evaluate the real-world performance of our attacks using four modern commercial off-the-shelf phones and two representative, commercial network carriers. We collect over 60 hours of traffic between the phones and the mobile networks and execute 160 VoLTE calls, which we use to successfully identify patterns in the physical layer parameter allocation and in VoLTE traffic, respectively. Our real-world experiments show that our mobile-relay works as expected in all test cases, and the VoLTE activity logs recovered describe the actual communication with 100% accuracy. Finally, we show that we can link network identifiers such as International Mobile Subscriber Identities (IMSI), Subscriber Concealed Identifiers (SUCI) and/or Globally Unique Temporary Identifiers (GUTI) to phone numbers while remaining undetected by the victim.

Virtual Private Networks (VPNs) can help people protect their privacy. Despite this, VPNs are not widely used among the public. In this survey study about the adoption and usage of VPNs, we investigate people’s motivation to use VPNs and the barriers they encounter in adopting them. Using data from 90 technologically savvy participants, we find that while nearly all (98%; 88) of the participants have knowledge about what VPNs are, less than half (42%; 37) have ever used VPNs primarily as a privacy-enhancing technology. Of these, 18% (7) abandoned using VPNs while 81% (30) continue to use them to protect their privacy online. In a qualitative analysis of survey responses, we find that people who adopt and continue to use VPNs for privacy purposes are primarily motivated by emotional considerations, including the strong desire to protect their privacy online, wide fear of surveillance and data tracking not only from Internet service providers (ISPs) but also governments and Internet corporations such as Facebook and Google. In contrast, people who are mainly motivated by practical considerations are more likely to abandon VPNs, especially once their practical need no longer exists. These people cite their access to alternative technologies and the effort required to use a VPN as reasons for abandonment. We discuss implications of these findings and provide suggestions on how to maximize adoption of privacy-enhancing technologies such as VPNs, focusing on how to align them with people’s interests and privacy risk evaluation.

Searchable Encryption (SE) allows a user to upload data to the cloud and to search it in a remote fashion while preserving the privacy of both the data and the queries. Recent research results describe attacks on SE schemes using the access pattern, denoting the ids of documents matching search queries, which most SE schemes reveal during query processing. However SE schemes usually leak more than just the access pattern, and this extra leakage can lead to attacks (much) more harmful than the ones using basic access pattern leakage only. We remark that in the special case of Multi-User Searchable Encryption (MUSE), where many users upload and search data in a cloud-based infrastructure, a large number of existing solutions have a common leakage in addition to the well-studied access pattern leakage. We show that this

Many browser cache attacks have been proposed in the literature to sniff the user’s browsing history. All of them rely on specific time measurements to infer if a resource is in the cache or not. Unlike the state-of-the-art, this paper reports on a novel cache-based attack that is not a timing attack but that abuses the HTTP cache-control and expires headers to extract the exact date and time when a resource was cached by the browser. The privacy implications are serious as this information can not only be utilized to detect if a website was visited by the user but it can also help build a timeline of the user’s visits. This goes beyond traditional history sniffing attacks as we can observe patterns of visit and model user’s behavior on the web. To evaluate the impact of our attack, we tested it on all major browsers and found that all of them, except the ones based on WebKit, are vulnerable to it. Since our attack requires specific HTTP headers to be present, we also crawled the T ranco Top 100K websites and identified 12, 970 of them can be detected with our approach. Among them, 1, 910 deliver resources that have expiry dates greater than 100 days, enabling long-term user tracking. Finally, we discuss possible defenses at both the browser and standard levels to prevent users from being tracked.

With the emergence of remote education and work in universities due to COVID-19, the 'zoomification' of higher education, i.e., the migration of universities to the clouds, reached the public discourse. Ongoing discussions reason about how this shift will take control over students' data away from universities, and may ultimately harm the privacy of researchers and students alike. However, there has been no comprehensive measurement of universities' use of public clouds and reliance on Software-as-a-Service offerings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and October 2022. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K., the Netherlands, THE Top100) frequently outsources universities' core functions and services---starting long before the COVID-19 pandemic. We attribute this clustering to several socio-economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals' privacy towards questions of academic independence and integrity.

Despite the Aircraft Communications, Addressing and Reporting System (ACARS) being widely deployed for over twenty years, little scrutiny has been applied to it outside of the aviation community. Whilst originally utilized by commercial airlines to track their flights and provide automated timekeeping on crew, today it serves as a multi-purpose air-ground data link for many aviation stakeholders including private jet owners, state actors and military. Such a change has caused ACARS to be used far beyond its original mandate; to date no work has been undertaken to assess the extent of this especially with regard to privacy and the various stakeholder groups which use it. In this paper, we present an analysis of ACARS usage by privacy sensitive actors-military, government and business. We conduct this using data from the VHF (both traditional ACARS, and VDL mode 2) and satellite communications subnetworks. Based on more than two million ACARS messages collected over the course of 16 months, we demonstrate that current ACARS usage systematically breaches location privacy for all examined aviation stakeholder groups, explaining the types of messages used to cause this problem.We illustrate the challenges with three case studies-one for each stakeholder group-to show how much privacy sensitive information can be constructed with a handful of ACARS messages. We contextualize our findings with opinions on the issue of privacy in ACARS from 40 aviation industry professionals. From this, we explore recommendations for how to address these issues, including use of encryption and policy measures.

Smartphone manufacturer provided default features (e.g., default location services, iCloud, Google Assistant, ad tracking) enhance the usability and extend the functionality of these devices. Prior studies have highlighted smartphone vulnerabilities and how users’ data can be harvested without their knowledge. However, little is known about manufacturer provided default features in this regard—their usability concerning configuring them during usage, and how users perceive them with regards to privacy. To bridge this gap, we conducted a task-based study with 27 Android and iOS smart-phone users in order to learn about their perceptions, concerns and practices, and to understand the usability of these features with regards to privacy. We explored the following: users’ awareness of these features, why and when do they change the settings of these features, the challenges they face while configuring these features, and finally the mitigation strategies they adopt. Our findings reveal that users of both platforms have limited awareness of these features and their privacy implications. Awareness of these features does not imply that a user can easily locate and adjust them when needed. Furthermore, users attribute their failure to configure default features to hidden controls and insufficient knowledge on how to configure them. To cope with difficulties of finding controls, users employ various coping strategies, some of which are platform specific but most often applicable to both platforms. However, some of these coping strategies leave users vulnerable.

The use of Open Source Intelligence (OSINT) to monitor and detect cybersecurity threats is gaining popularity among Cybersecurity Emergency or Incident Response Teams (CERTs/CSIRTs). They increasingly use semi-automated OSINT approaches when monitoring cyber threats for public infrastructure services and incident response. Most of the systems use publicly available data, often focusing on social media due to timely data for situational assessment. As indirect and affected stakeholders, the acceptance of OSINT systems by users, as well as the conditions which influence the acceptance, are relevant for the development of OSINT systems for cybersecurity. Therefore, as part of the ethical and social technology assessment, we conducted a survey (N=1,093), in which we asked participants about their acceptance of OSINT systems, their perceived need for open source surveillance, as well as their privacy behavior and concerns. Further, we tested if the awareness of OSINT is an interactive factor that affects other factors. Our results indicate that cyber threat perception and the perceived need for OSINT are positively related to acceptance, while privacy concerns are negatively related. The awareness of OSINT, however, has only shown effects on people with higher privacy concerns. Here, particularly high OSINT awareness and limited privacy concerns were associated with higher OSINT acceptance. Lastly, we provide implications for further research and the use of OSINT systems for cybersecurity by authorities. As OSINT is a framework rather than a single technology, approaches can be selected and combined to adhere to data minimization and anonymization as well as to leverage improvements in privacy-preserving computation and machine learning innovations. Regarding the use of OSINT, the results suggest to favor approaches that provide transparency to users regarding the use of the systems and the data they gather.

In this paper, we describe efficient protocols to perform in parallel many reads and writes in private arrays according to private indices. The protocol is implemented on top of the Arithmetic Black Box (ABB) and can be freely composed to build larger privacypreserving applications. For a large class of secure multiparty computation (SMC) protocols, our technique has better practical and asymptotic performance than any previous ORAM technique that has been adapted for use in SMC. Our ORAM technique opens up a large class of parallel algorithms for adoption to run on SMC platforms. In this paper, we demonstrate how the minimum spanning tree (MST) finding algorithm by Awerbuch and Shiloach can be executed without revealing any details about the underlying graph (beside its size). The data accesses of this algorithm heavily depend on the location and weight of edges (which are private) and our ORAM technique is instrumental in their execution. Our implementation is the first-ever realization of a privacypreserving MST algorithm with sublinear round complexity.

Intel SGX has been a popular trusted execution environment (TEE) for protecting the integrity and confidentiality of applications running on untrusted platforms such as cloud. However, the access patterns of SGX-based programs can still be observed by adversaries, which may leak important information for successful attacks. Researchers have been experimenting with Oblivious RAM (ORAM) to address the privacy of access patterns. ORAM is a powerful low-level primitive that provides application-agnostic protection for any I/O operations, however, at a high cost. We find that some application-specific access patterns, such as sequential block I/O, do not provide additional information to adversaries. Others, such as sorting, can be replaced with specific oblivious algorithms that are more efficient than ORAM. The challenge is that developers may need to look into all the details of application-specific access patterns to design suitable solutions, which is time-consuming and error-prone. In this paper, we present the lightweight SGX based MapReduce (SGX-MR) approach that regulates the dataflow of data-intensive SGX applications for easier application-level access-pattern analysis and protection. It uses the MapReduce framework to cover a large class of data-intensive applications, and the entire framework can be implemented with a small memory footprint. With this framework, we have examined the stages of data processing, identified the access patterns that need protection, and designed corresponding efficient protection methods. Our experiments show that SGX-MR based applications are much more efficient than the ORAM-based implementations.

We study the problem of privacy-preserving approximate kNN search in an outsourced environment — the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious map. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. We implement, evaluate efficiency, and compare the performance of two concrete schemes based on an oblivious AVL tree and an oblivious BSkiplist.

Several data protection regulations permit individuals to request all personal information that an organization holds about them by utilizing Subject Access Requests (SARs). Prior work has observed the identification process of such requests, demonstrating weak policies that are vulnerable to potential data breaches. In this paper, we analyze and compare prior work in terms of methodologies, requested identification credentials and threat models in the context of privacy and cybersecurity. Furthermore, we have devised a longitudinal study in which we examine the impact of responsible disclosures by re-evaluating the SAR authentication processes of 40 organizations after they had two years to improve their policies. Here, we demonstrate that 53% of the previously vulnerable organizations have not corrected their policy and an additional 27% of previously non-vulnerable organizations have potentially weakened their policies instead of improving them, thus leaking sensitive personal information to potential adversaries. To better understand state-of-the-art SAR policies, we interviewed several Data Protection Officers and explored the reasoning behind their processes from a viewpoint in the industry and gained insights about potential criminal abuse of weak SAR policies. Finally, we propose several technical modifications to SAR policies that reduce privacy and security risks of data controllers.

Providing unrestricted access to sensitive content such as news and software is difficult in the presence of adaptive and resourceful surveillance and censoring adversaries. In this paper we leverage the distributed and resilient nature of commercial Satoshi blockchains to develop the first provably secure, censorship resistant, cost-efficient storage system with anonymous and private access, built on top of commercial cryptocurrency transactions. We introduce max-rate transactions, a practical construct to persist data of arbitrary size entirely in a Satoshi blockchain. We leverage max-rate transactions to develop UWeb, a blockchain-based storage system that charges publishers to self-sustain its decentralized infrastructure. UWeb organizes blockchain-stored content for easy retrieval, and enables clients to store and access content with provable anonymity, privacy and censorship resistance properties. We present results from UWeb experiments with writing 268.21 MB of data into the live Litecoin blockchain, including 4.5 months of live-feed BBC articles, and 41 censorship resistant tools. The max-rate writing throughput (183 KB/s) and blockchain utilization (88%) exceed those of state-of-the-art solutions by 2-3 orders of magnitude and broke Litecoin’s record of the daily average block size. Our simulations with up to 3,000 concurrent UWeb writers confirm that UWeb does not impact the confirmation delays of financial transactions.