Remote Data Checking (RDC) is a technique by which clients can establish that data outsourced at untrusted servers remains intact over time. RDC is useful as a prevention tool, allowing clients to periodically check if data has been damaged, and as a repair tool whenever damage has been detected. Initially proposed in the context of a single server, RDC was later extended to verify data integrity in distributed storage systems that rely on replication and on erasure coding to store data redundantly at multiple servers. Recently, a technique was proposed to add redundancy based on network coding, which offers interesting tradeoffs because of its remarkably low communication overhead to repair corrupt servers. Unlike previous work on RDC which focused on minimizing the costs of the prevention phase, we take a holistic look and initiate the investigation of RDC schemes for distributed systems that rely on network coding to minimize the combined costs of both the prevention and repair phases. We propose RDC-NC, a novel secure and efficient RDC scheme for network coding-based distributed storage systems. RDC-NC mitigates new attacks that stem from the underlying principle of network coding. The scheme is able to preserve in an adversarial setting the minimal communication overhead of the repair component achieved by network coding in a benign setting. We implement our scheme and experimentally show that it is computationally inexpensive for both clients and servers. © 2010 ACM

Remote Data Checking (RDC) allows clients to efficiently check the integrity of data stored at untrusted servers. This allows data owners to assess the risk of outsourcing data in the cloud, making RDC a valuable tool for data auditing. A robust RDC scheme incorporates mechanisms to mitigate arbitrary amounts of data corruption. In particular, protection against small corruptions (i.e., bytes or even bits) ensures that attacks that modify a few bits do not destroy an encrypted file or invalidate authentication information. Early RDC schemes have focused on static data, whereas later schemes such as DPDP support the full range of dynamic operations on the outsourced data, including insertions, modifications, and deletions. Robustness is required for both static and dynamic RDC schemes that rely on spot checking for efficiency. However, under an adversarial setting there is a fundamental tension between efficient dynamic updates and the encoding required to achieve robustness, because updating even a small portion of the file may require retrieving the entire file. We identify the challenges that need to be overcome when trying to add robustness to a DPDP scheme. We propose the first RDC schemes that provide robustness and, at the same time, support dynamic updates, while requiring small, constant, client storage. Our first construction is efficient in encoding, but has a high communication cost for updates. Our second construction overcomes this drawback through a combination of techniques that includes RS codes based on Cauchy matrices, decoupling the encoding for robustness from the position of symbols in the file, and reducing insert/delete operations to append/modify operations when updating the RS-encoded parity data.

Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger data owners’ privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA and Sarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved in the personal computing devices (i.e., confidentiality during their lifetime); and also, they should be rendered irrecoverable after they are removed from the devices (i.e., confidentiality after their lifetime). Encryption and secure deletion are used to ensure data confidentiality during and after their lifetime, respectively. This work aims to perform a thorough literature review on the techniques being used to protect confidentiality of the data in personal computing devices, including both encryption and secure deletion. Especially for encryption, we mainly focus on the novel plausibly deniable encryption (PDE), which can ensure data confidentiality against both a coercive (i.e., the attacker can coerce the data owner for the decryption key) and a non-coercive attacker.

Named data networking (NDN) shifts today's host-centric Internet architecture to a new data-centric network architecture. This well suits the increasingly mobile and information-intensive applications that dominate today's Internet. NDN allows routers to cache named content, leading to a significant improvement of content retrieval which, however, opens a door for many new attacks. In this work, we focus on content poisoning attack, the content poisoned by an attacker will be cached and propagated in the NDN network. Existing solutions unfortunately cannot work when the NDN routers are compromised by attackers. We propose ROM, the Router-Oriented Mitigation of content poisoning attack in NDN, offering security guarantees even when the routers are malicious. ROM defends against content poisoning attack by temporarily excluding the malicious routers from transmission path, eliminating (or significantly reducing) the possibility that the content will be poisoned during transmission. However, localizing malicious routers in NDN is very challenging due to NDN's distributed nature and the lack of global identifiers. We attack this issue from a new angle. We introduce reputation for each NDN router, and forward content based on the reputation. A router with a better reputation will be more likely honest and has a higher probability to be included into the transmission path. In addition, we design a novel mechanism to quantify the reputation value by utilizing our unique observations for NDN. Security analysis and simulations performed in ndnSIM demonstrate that ROM can mitigate the content poisoning attack with high efficiency and excellent accuracy.

Ransomware attacks are becoming prevalent nowadays with the flourishing of crypto-currencies. As the most harmful variant of ransomware crypto-ransomware encrypts the victim's valuable data, and asks for ransom money. Paying the ransom money, however, may not guarantee recovery of the data being encrypted. Most of the existing work for ransomware defense purely focuses on ransomware detection. A few of them consider data recovery from ransomware attacks, but they are not able to defend against ransomware which can obtain a high system privilege. In this work, we design RDS3, a novel Ransomware Defense Strategy, in which we Stealthily back up data in the Spare space of a computing device, such that the data encrypted by ransomware can be restored. Our key idea is that the spare space which stores the backup data is fully isolated from the ransomware. In this way, the ransomware is not able to “touch” the backup data regardless of what privilege it can obtain. Security analysis and experimental evaluation show that RDS3 can mitigate ransomware attacks with an acceptable overhead.