Conference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security最新文献

Research on side-channel leaks has long been focusing on the information exposure from a single channel (memory, network traffic, power, etc.). Less studied is the risk of learning from multiple side channels related to a target activity (e.g., website visits) even when individual channels are not informative enough for an effective attack. Although the prior research made the first step on this direction, inferring the operations of foreground apps on iOS from a set of global statistics, still less clear are how to determine the maximum information leaks from all target-related side channels on a system, what can be learnt about the target from such leaks and most importantly, how to control information leaks from the whole system, not just from an individual channel. To answer these fundamental questions, we performed the first systematic study on multi-channel inference, focusing on iOS as the first step. Our research is based upon a novel attack technique, called Mischief, which given a set of potential side channels related to a target activity (e.g., foreground apps), utilizes probabilistic search to approximate an optimal subset of the channels exposing most information, as measured by Merit Score, a metric for correlation-based feature selection. On such an optimal subset, an inference attack is modeled as a multivariate time series classification problem, so the state-of-the-art deep-learning based solution, InceptionTime in particular, can be applied to achieve the best possible outcome. Mischief is found to work effectively on today's iOS (16.2), identifying foreground apps, website visits, sensitive IoT operations (e.g., opening the door) with a high confidence, even in an open-world scenario, which demonstrates that the protection Apple puts in place against the known attack is inadequate. Also importantly, this new understanding enables us to develop more comprehensive protection, which could elevate today's side-channel research from suppressing leaks from individual channels to controlling information exposure across the whole system.

Public release of wrist-worn motion sensor data is growing. They enable and accelerate research in developing new algorithms to passively track daily activities, resulting in improved health and wellness utilities of smartwatches and activity trackers. But, when combined with sensitive attribute inference attack and linkage attack via re-identification of the same user in multiple datasets, undisclosed sensitive attributes can be revealed to unintended organizations with potentially adverse consequences for unsuspecting data contributing users. To guide both users and data collecting researchers, we characterize the re-identification risks inherent in motion sensor data collected from wrist-worn devices in users' natural environment. For this purpose, we use an open-set formulation, train a deep learning architecture with a new loss function, and apply our model to a new data set consisting of 10 weeks of daily sensor wearing by 353 users. We find that re-identification risk increases with an increase in the activity intensity. On average, such risk is 96% for a user when sharing a full day of sensor data.

Identifying anomalies in data is central to the advancement of science, national security, and finance. However, privacy concerns restrict our ability to analyze data. Can we lift these restrictions and accurately identify anomalies without hurting the privacy of those who contribute their data? We address this question for the most practically relevant case, where a record is considered anomalous relative to other records. We make four contributions. First, we introduce the notion of sensitive privacy, which conceptualizes what it means to privately identify anomalies. Sensitive privacy generalizes the important concept of differential privacy and is amenable to analysis. Importantly, sensitive privacy admits algorithmic constructions that provide strong and practically meaningful privacy and utility guarantees. Second, we show that differential privacy is inherently incapable of accurately and privately identifying anomalies; in this sense, our generalization is necessary. Third, we provide a general compiler that takes as input a differentially private mechanism (which has bad utility for anomaly identification) and transforms it into a sensitively private one. This compiler, which is mostly of theoretical importance, is shown to output a mechanism whose utility greatly improves over the utility of the input mechanism. As our fourth contribution we propose mechanisms for a popular definition of anomaly ((β, r)-anomaly) that (i) are guaranteed to be sensitively private, (ii) come with provable utility guarantees, and (iii) are empirically shown to have an overwhelmingly accurate performance over a range of datasets and evaluation criteria.

Side-channel risks of Intel's SGX have recently attracted great attention. Under the spotlight is the newly discovered page-fault attack, in which an OS-level adversary induces page faults to observe the page-level access patterns of a protected process running in an SGX enclave. With almost all proposed defense focusing on this attack, little is known about whether such efforts indeed raises the bar for the adversary, whether a simple variation of the attack renders all protection ineffective, not to mention an in-depth understanding of other attack surfaces in the SGX system. In the paper, we report the first step toward systematic analyses of side-channel threats that SGX faces, focusing on the risks associated with its memory management. Our research identifies 8 potential attack vectors, ranging from TLB to DRAM modules. More importantly, we highlight the common misunderstandings about SGX memory side channels, demonstrating that high frequent AEXs can be avoided when recovering EdDSA secret key through a new page channel and fine-grained monitoring of enclave programs (at the level of 64B) can be done through combining both cache and cross-enclave DRAM channels. Our findings reveal the gap between the ongoing security research on SGX and its side-channel weaknesses, redefine the side-channel threat model for secure enclaves, and can provoke a discussion on when to use such a system and how to use it securely.