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Cloud file systems offer organizations a scalable and reliable file storage solution. However, cloud file systems have become prime targets for adversaries, and traditional designs are not equipped to protect organizations against the myriad of attacks that may be initiated by a malicious cloud provider, co-tenant, or end-client. Recently proposed designs leveraging cryptographic techniques and trusted execution environments (TEEs) still force organizations to make undesirable trade-offs, consequently leading to either security, functional, or performance limitations. In this paper, we introduce TFS, a cloud file system that leverages the security capabilities provided by TEEs to bootstrap new security protocols that meet real-world security, functional, and performance requirements. Through extensive security and performance analyses, we show that TFS can ensure stronger security guarantees while still providing practical utility and performance w.r.t. state-of-the-art systems; compared to the widely-used NFS, TFS achieves up to 2.1X speedups across micro-benchmarks and incurs

Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users' knowledge or consent -- violating the core tenet of user privacy. In this paper, we present CaCTUs, a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2s at a frame rate of 10fps and a resolution of 480p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data., Accepted to PETS (Privacy Enhancing Technologies Symposium) 2022

Public clouds provide impressive capability through resource sharing. However, recent works have shown that the reuse of IP addresses can allow adversaries to exploit the latent configurations left by previous tenants. In this work, we perform a comprehensive analysis of the effect of cloud IP address allocation on exploitation of latent configuration. We first develop a statistical model of cloud tenant behavior and latent configuration based on literature and deployed systems. Through these, we analyze IP allocation policies under existing and novel threat models. Our resulting framework, EIPSim, simulates our models in representative public cloud scenarios, evaluating adversarial objectives against pool policies. In response to our stronger proposed threat model, we also propose IP scan segmentation, an IP allocation policy that protects the IP pool against adversarial scanning even when an adversary is not limited by number of cloud tenants. Our evaluation shows that IP scan segmentation reduces latent configuration exploitability by 97.1% compared to policies proposed in literature and 99.8% compared to those currently deployed by cloud providers. Finally, we evaluate our statistical assumptions by analyzing real allocation and configuration data, showing that results generalize to deployed cloud workloads. In this way, we show that principled analysis of cloud IP address allocation can lead to substantial security gains for tenants and their users.

Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users' knowledge or consent -- violating the core tenet of user privacy. In this paper, we introduce CaCTUs, a privacy-preserving smart camera system that returns control to the user; the root of trust begins with the user and is maintained through a series of cryptographic protocols designed to support popular features, such as sharing, deleting, and viewing videos live. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data. We then discuss how our techniques and protocols can also be extended to privacy-preserving designs of other IoT devices recording time series data., Extension of arXiv:2201.09338

Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new challenges; failure to correctly provision, maintain, or decommission elastic services can lead to functional failure and vulnerability to attack. In this paper, we explore a broad class of attacks on clouds which we refer to as cloud squatting. In a cloud squatting attack, an adversary allocates resources in the cloud (e.g., IP addresses) and thereafter leverages latent configuration to exploit prior tenants. To measure and categorize cloud squatting we deployed a custom Internet telescope within the Amazon Web Services us-east-1 region. Using this apparatus, we deployed over 3 million servers receiving 1.5 million unique IP addresses (56% of the available pool) over 101 days beginning in March of 2021. We identified 4 classes of cloud services, 7 classes of third-party services, and DNS as sources of exploitable latent configurations. We discovered that exploitable configurations were both common and in many cases extremely dangerous; we received over 5 million cloud messages, many containing sensitive data such as financial transactions, GPS location, and PII. Within the 7 classes of third-party services, we identified dozens of exploitable software systems spanning hundreds of servers (e.g., databases, caches, mobile applications, and web services). Lastly, we identified 5446 exploitable domains spanning 231 eTLDs-including 105 in the top 10,000 and 23 in the top 1000 popular domains. Through tenant disclosures we have identified several root causes, including (a) a lack of organizational controls, (b) poor service hygiene, and (c) failure to follow best practices. We conclude with a discussion of the space of possible mitigations and describe the mitigations to be deployed by Amazon in response to this study.