Abstract Protecting sensitive data stored on local storage devices e.g., laptops, tablets etc. is essential for privacy. When adversaries are powerful enough to coerce users to reveal encryption keys/passwords, encryption alone becomes insufficient for data protection. Additional mechanisms are required to hide the very presence of sensitive data. Plausibly deniable storage systems (PDS) are designed to defend against such powerful adversaries. Plausible deniability allows a user to deny the existence of certain stored data even when an adversary has access to the storage medium. However, existing plausible deniability solutions leave users at the mercy of adversaries suspicious of their very use. Indeed, it may be difficult to justify the use of a plausible deniability system while claiming that no sensitive data is being hidden. This work introduces INFUSE, a plausibly-deniable file system that hides not only contents but also the evidence that a particular system is being used to hide data. INFUSE is “invisible” (identical layout with standard file system), provides redundancy, handles overwrites, survives data loss, and is secure in the presence of multi-snapshot adversaries. INFUSE is efficient. Public data operations are orders of magnitude faster than existing multi-snapshot resilient PD systems, and only 15% slower than a standard non-PD baseline, and hidden data operations perform comparably to existing systems.

Abstract Encryption protects sensitive data from unauthorized access, yet is not sufficient when users are forced to surrender keys under duress. In contrast, plausible deniability enables users to not only encrypt data but also deny its existence when challenged. Most existing plausible deniability work (e.g. the successful and unfortunately now-defunct TrueCrypt) tackles “single snapshot” adversaries, and cannot handle the more realistic scenario of adversaries gaining access to a device at multiple time points. Such “multi-snapshot” adversaries can simply observe modifications between snapshots and detect the existence of hidden data. Existing ideas handling “multi-snapshot” scenarios feature prohibitive overheads when deployed on practically-sized disks. This is mostly due to a lack of data locality inherent in certain standard access-randomization mechanisms, one of the building blocks used to ensure plausible deniability. In this work, we show that such randomization is not necessary for strong plausible deniability. Instead, it can be replaced by a canonical form that permits most of writes to be done sequentially. This has two key advantages: 1) it reduces the impact of seek due to random accesses; 2) it reduces the overall number of physical blocks that need to be written for each logical write. As a result, PD-DM increases I/O throughput by orders of magnitude (10–100× in typical setups) over existing work while maintaining strong plausible deniability against multi-snapshot adversaries. Notably, PD-DM is the first plausible-deniable system getting within reach of the performance of standard encrypted volumes (dm-crypt) for random I/O.

Abstract Sensitive information is present on our phones, disks, watches and computers. Its protection is essential. Plausible deniability of stored data allows individuals to deny that their device contains a piece of sensitive information. This constitutes a key tool in the fight against oppressive governments and censorship. Unfortunately, existing solutions, such as the now defunct TrueCrypt [5], can defend only against an adversary that can access a user’s device at most once (“single-snapshot adversary”). Recent solutions have traded significant performance overheads for the ability to handle more powerful adversaries able to access the device at multiple points in time (“multi-snapshot adversary”). In this paper we show that this sacrifice is not necessary. We introduce and build DataLair1, a practical plausible deniability mechanism. When compared with existing approaches, DataLair is two orders of magnitude faster for public data accesses, and 5 times faster for hidden data accesses. An important component in DataLair is a new write-only ORAM construction which improves on the complexity of the state of the art write-only ORAM by a factor ofO(logN), where N denotes the underlying storage disk size.

Sensitive information is present on our phones, disks, watches and computers. Its protection is essential. Plausible deniability of stored data allows individuals to deny that their device contains a piece of sensitive information. This constitutes a key tool in the fight against oppressive governments and censorship.

Kernel rootkits often hide associated malicious processes by altering reported task struct information to upper layers and applications such as ps and top. Virtualized settings offer a unique opportunity to mitigate this behavior using dynamic virtual machine introspection (VMI). For known kernels, VMI can be deployed to search for kernel objects and identify them by using unique data structure "signatures".

"Green" and its "low power" cousin are the new hot spots in computing. In cloud data centers, at scale, ideas of deploying low-power ARM architectures or even large numbers of extremely "wimpy" nodes [1, 2] seem increasingly appealing. Skeptics on the other hand maintain that we cannot get more than what we pay for and no free lunches can be had. In this paper we explore these theses and provide insights into the power-performance trade-off at scale for "wimpy", back-to basics, power-efficient RISC architectures. We use ARM as modern proxy for these and quantify the cost/performance ratio precisely-enough to allow for a broader conclusion. We then offer an intuition as to why this may still hold in 2030.