Browser fingerprinting is the practice of tracking users across the Web by collecting attributes from their devices and combining them to create unique identifiers. This practice poses major privacy risks to users, and more than a decade of research has quantified fingerprinting risks due to various attributes, leading browser developers to implement many privacy-enhancing changes. Early work used Shannon entropy to quantify risks. However, Shannon entropy can grow with dataset size, limiting the ability to compare datasets and results. Researchers then introduced normalized entropy as a measure for comparing browser fingerprinting datasets of different sizes and numerous works followed using normalized entropy for this purpose. We identify and address a resulting problem in the fingerprinting literature. We show normalized entropy is ill-suited to compare datasets of different sizes --- it decreases as dataset size increases. We show this both analytically and empirically, leveraging a recently published dataset of browser attributes commonly used for fingerprinting. Given the unmet need for a better fingerprinting risk measure, we define a minimal set of desired properties for such a measure: scale-invariance, monotonicity and estimability. We then propose to use Tsallis entropy as a more interpretable fingerprinting risk measure. We evaluate Shannon, normalized, and Tsallis entropy with respect to the properties, and prove that only Tsallis entropy satisfies all of them.

Browser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users' devices to create unique 'fingerprints'. This technique and resulting privacy risks have been studied for over a decade. Yet further research is limited because prior studies used data not publicly available. Additionally, data in prior studies lacked user demographics. Here we provide a first-of-its-kind dataset to enable further research. It includes browser attributes with users' demographics and survey responses, collected with informed consent from 8,400 US study participants. We use this dataset to demonstrate how fingerprinting risks differ across demographic groups. For example, we find lower income users are more at risk, and find that as users' age increases, they are both more likely to be concerned about fingerprinting and at real risk of fingerprinting. Furthermore, we demonstrate an overlooked risk: user demographics, such as gender, age, income level and race, can be inferred from browser attributes commonly used for fingerprinting, and we identify which browser attributes most contribute to this risk. Our data collection process also conducted an experiment to study what impacts users' likelihood to share browser data for open research, in order to inform future data collection efforts, with responses from 12,461 total participants. Female participants were significantly less likely to share their browser data, as were participants who were shown the browser data we asked to collect. Overall, we show the important role of user demographics in the ongoing work that intends to assess fingerprinting risks and improve user privacy, with findings to inform future privacy enhancing browser developments. The dataset and data collection tool we provide can be used to further study research questions not addressed in this work.

A complex problem when outsourcing data to the cloud is access control management. Encryption, by wrapping data with a self-enforcing protection layer, provides access control enforcement by making resources intelligible only to users holding the necessary key. The real challenge becomes then the efficient revocation of access. We address this challenge and present an approach to effectively and efficiently enforce access revocation on resources stored at external cloud providers. The approach relies on a resource transformation that provides strong mutual inter-dependency in its encrypted representation. To revoke access on a resource, it is then sufficient to update a small portion of it, with the guarantee that the resource as a whole (and any portion of it) will become unintelligible to those from whom access is revoked. Our experimental results show the effectiveness of our approach, and confirm its efficiency, especially when managing large resources with dynamic access policy.

A Time-Lock enables the release of a secret at a future point in time. Many approaches implement Time-Locks as cryptographic puzzles, binding the recovery of the secret to the solution of the puzzle. Since the time required to find the puzzle’s solution may vary due to a multitude of factors, including the computational effort spent, these solutions may not suit all scenarios.

Decentralized cloud storage services represent a promising opportunity for a different cloud market, meeting the supply and demand for IT resources of an extensive community of users. The dynamic and independent nature of the resulting infrastructure introduces security concerns that can represent a slowing factor toward the realization of such an opportunity, otherwise clearly appealing and promising for the expected economic benefits. In this paper, we present an approach enabling resource owners to effectively protect and securely delete their resources while relying on decentralized cloud services for their storage. Our solution combines All-Or-Nothing-Transform for strong resource protection, and carefully designed strategies for slicing resources and for their decentralized allocation in the storage network. We address both availability and security guarantees, jointly considering them in our model and enabling resource owners to control their setting.

Decentralized Cloud Storage (DCS) networks represent an interesting solution for data storage and management. DCS networks rely on the voluntary effort of a considerable number of (possibly untrusted) nodes, which may dynamically join and leave the network at any time. To profitably rely on DCS for data storage, data owners therefore need solutions that guarantee confidentiality and availability of their data. In this paper, we present an approach enabling data owners to keep data confidentiality and availability under control, limiting the owners intervention with corrective actions when availability or confidentiality is at risk. Our approach is based on the combined adoption of AONT (All-Or-Nothing-Transform) and fountain codes. It provides confidentiality of outsourced data also against malicious coalitions of nodes, and guarantees data availability even in case of node failures. Our experimental evaluation clearly shows the benefits of using fountain codes with respect to other approaches adopted by current DCS networks.

The distributed shuffle index strengthens the guarantees of access confidentiality provided by the shuffle index through the distribution of data among three cloud providers. In this paper, we analyze architectural and design issues and describe an implementation of the distributed shuffle index integrated with different cloud providers (i.e., Amazon S3, OpenStack Swift, Google Cloud Storage, and EMC Elastic Cloud Storage). The experimental results obtained with our implementation confirm the protection guarantees provided by the distributed shuffle index and its limited performance overhead, demonstrating its practical applicability in cloud scenarios.

We present an approach to enforce access revocation on resources stored at external cloud providers. The approach relies on a resource transformation that provides strong mutual inter-dependency in its encrypted representation. To revoke access on a resource, it is then sufficient to update a small portion of it, with the guarantee that the resource as a whole (and any portion of it) will become unintelligible to those from whom access is revoked. The extensive experimental evaluation on a variety of configurations confirmed the effectiveness and efficiency of our solution, which showed excellent performance and compatibility with several implementation strategies.

SQLite is the most widely deployed in-process library that implements a SQL database engine. It offers high storage efficiency, fast query operation and small memory needs. Due to the fact that a complete SQLite database is stored in a single cross-platform disk file and SQLite does not support multiple users, anyone who has direct access to the file can read the whole database content. SELinux was originally developed as a Mandatory Access Control (MAC) mechanism for Linux to demonstrate how to overcome DAC limitations. However, SELinux provides per-file protection, thus the database file is treated as an atomic unit, impeding the definition of a fine-grained mandatory access control (MAC) policy for database objects.

Android has recently introduced the support for Mandatory Access Control, which extends previous security services relying on the Android Permission Framework and on the kernel-level Discretionary Access Control. This extension has been obtained with the use of SELinux and its adaptation to Android (SEAndroid). Currently, the use of the MAC model is limited to the protection of system resources. All the apps that are installed by users fall in a single undifferentiated domain, untrusted_app. We propose an extension of the architecture that permits to associate with each app a dedicated MAC policy, contained in a dedicated appPolicyModule, in order to protect app resources even from malware with root privileges.