A hard lesson: Assessing the HTTPS deployment of Italian university websites

In this paper we carry out a systematic analysis of the state of the HTTPS deployment of the most popular Italian university websites. Our analysis focuses on three different key aspects: HTTPS adoption and activation, HTTPS certificates, and cryptographic TLS implementations. Our investigation shows that the current state of the HTTPS deployment is unsatisfactory, yet it is possible to significantly improve the level of security by working exclusively at the web application layer. We hope this observation will encourage site operators to take actions to improve the current state of protection

Identifying Authorship Style in Malicious Binaries: Techniques, Challenges & Datasets

Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to identify authorship style. Our survey explores malicious author style and the adversarial techniques used by them to remain anonymous. We examine the adversarial impact on the state-of-the-art methods. We identify key findings and explore the open research challenges. To mitigate the lack of ground truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 15,660 malware labeled to 164 threat actor groups

SoK: Privacy-Preserving Signatures

Modern security systems depend fundamentally on the ability of users to authenticate their communications to other parties in a network. Unfortunately, cryptographic authentication can substantially undermine the privacy of users. One possible solution to this problem is to use privacy-preserving cryptographic authentication. These protocols allow users to authenticate their communications without revealing their identity to the verifier. In the non-interactive setting, the most common protocols include blind, ring, and group signatures, each of which has been the subject of enormous research in the security and cryptography literature. These primitives are now being deployed at scale in major applications, including Intel\u27s SGX software attestation framework. The depth of the research literature and the prospect of large-scale deployment motivate us to systematize our understanding of the research in this area. This work provides an overview of these techniques, focusing on applications and efficiency

Contingent payments on a public ledger: models and reductions for automated verification

International audienceWe study protocols that rely on a public ledger infrastructure, concentrating on protocols for zero-knowledge contingent payment, whose security properties combine diverse notions of fairness and privacy. We argue that rigorous models are required for capturing the ledger semantics, the protocol-ledger interaction, the cryptographic primitives and, ultimately, the security properties one would like to achieve.Our focus is on a particular level of abstraction, where network messages are represented by a term algebra, protocol execution by state transition systems (e.g. multiset rewrite rules) and where the properties of interest can be analyzed with automated verification tools. We propose models for: (1) the rules guiding the ledger execution, taking the coin functionality of public ledgers such as Bitcoin as an example; (2) the security properties expected from ledger-based zero-knowledge contingent payment protocols; (3) two different security protocols that aim at achieving these properties relying on different ledger infrastructures; (4) reductions that allow simpler term algebras for homomorphic cryptographic schemes.Altogether, these models allow us to derive a first automated verification for ledger-based zero-knowledge contingent payment using the Tamarin prover. Furthermore , our models help in clarifying certain underlying assumptions, security and efficiency tradeoffs that should be taken into account when deploying protocols on the blockchain

Making Your Program Oblivious: a Comparative Study for Side-channel-safe Confidential Computing

Trusted Execution Environments (TEEs) are gradually adopted by major cloud providers, offering a practical option of \emph{confidential computing} for users who don't fully trust public clouds. TEEs use CPU-enabled hardware features to eliminate direct breaches from compromised operating systems or hypervisors. However, recent studies have shown that side-channel attacks are still effective on TEEs. An appealing solution is to convert applications to be \emph{data oblivious} to deter many side-channel attacks. While a few research prototypes on TEEs have adopted specific data oblivious operations, the general conversion approaches have never been thoroughly compared against and tested on benchmark TEE applications. These limitations make it difficult for researchers and practitioners to choose and adopt a suitable data oblivious approach for their applications. To address these issues, we conduct a comprehensive analysis of several representative conversion approaches and implement benchmark TEE applications with them. We also perform an extensive empirical study to provide insights into their performance and ease of use

New Covert and Side Channels Based on Retirement

Intel processors utilize the retirement to orderly retire the micro-ops that have been executed out of order. To enhance retirement utilization, the retirement is dynamically shared between two logical cores on the same physical core. However, this shared retirement mechanism creates a potential vulnerability wherein an attacker can exploit the competition for retirement to infer the data of a victim on another logical core on the same physical core. Based on this leakage, we propose two new covert channels: the Different Instructions (DI) covert channel using different instructions for information transmission, and the Same Instructions (SI) covert channel using the same instructions to transmit information. The DI covert channel can achieve 98.5% accuracy with a bandwidth of 1450 Kbps, while the SI covert channel can achieve 94.85% accuracy with a bandwidth of 483.33 Kbps. Furthermore, this paper explores additional applications of retirement: Firstly, retirement is applied to Spectre attacks, resulting in a new variant of Spectre v1, which can achieve 94.17% accuracy with a bandwidth of 29 Kbps; Secondly, retirement is leveraged to infer the programs being executed by the victim, which can infer 10 integer benchmarks of SPEC with 89.28% accuracy. Finally, we discuss possible protection against new covert channels.Comment: 13 pages and 17 figure

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges

With the advent of functional encryption, new possibilities for computation on encrypted data have arisen. Functional Encryption enables data owners to grant third-party access to perform specified computations without disclosing their inputs. It also provides computation results in plain, unlike Fully Homomorphic Encryption. The ubiquitousness of machine learning has led to the collection of massive private data in the cloud computing environment. This raises potential privacy issues and the need for more private and secure computing solutions. Numerous efforts have been made in privacy-preserving machine learning (PPML) to address security and privacy concerns. There are approaches based on fully homomorphic encryption (FHE), secure multiparty computation (SMC), and, more recently, functional encryption (FE). However, FE-based PPML is still in its infancy and has not yet gotten much attention compared to FHE-based PPML approaches. In this paper, we provide a systematization of PPML works based on FE summarizing state-of-the-art in the literature. We focus on Inner-product-FE and Quadratic-FE-based machine learning models for the PPML applications. We analyze the performance and usability of the available FE libraries and their applications to PPML. We also discuss potential directions for FE-based PPML approaches. To the best of our knowledge, this is the first work to systematize FE-based PPML approaches