Video encryption and hiding technology play a crucial role in preserving both information security and privacy. Data hiding in encrypted video allows for marking and tampering detection of the encrypted stream while ensuring the security of the video content. In this case, the extraction process of embedded bits can be performed without decryption, which is more efficient. Owing to the limited research conducted on versatile video coding (VVC), this paper presents a large-capacity data hiding scheme in encrypted video based on H.266/VVC codec. In the proposed scheme, AES-CTR is employed to generate a pseudorandom binary sequence (PRBS). Then the PRBS is used to encrypt the syntax elements before context-based adaptive binary arithmetic coding (CABAC), including luma intra prediction mode (IPM), quantized transform coefficient (QTC) sign, and motion vector difference (MVD) sign. Simultaneously, QTC amplitude is utilized for data embedding. Here, a piecewise embedding method (PEM) is designed to improve the embedding capacity. Since the syntax elements used for encryption and data hiding are independent, the decryption and extraction processes are fully commutable. Experimental results demonstrated outstanding performance of the proposed scheme in embedding capacity while preserving the integrity and authenticity of the original video content.

Video encryption aims to protect video content in open networks. Although some existing algorithms ensure visual distortion, they are incapable to support fast-forward or rewind, and are vulnerable to chosen-plaintext attack (CPA). Based on the 2D-SCL chaotic map with dynamical parameters, a novel selective encryption algorithm (SEA) is proposed in this paper. To our knowledge, it is the first SEA with interactive characteristics designed for H.266/VVC video. The encryption and decryption process are independent for different network abstract layer (NAL) units, thus supporting decryption starting at any given frame in the video bitstream. Then the initial values and system parameters of the 2D-SCL map are closely related to the original video sequence, hence the proposed SEA is efficient to resist CPA. In addition, the confusion-diffusion mechanism is designed to further encrypt quantized transform coefficients (QTCs). Experimental results demonstrated outstanding performance of the proposed scheme in video security.

Since Bitcoin, blockchain has attracted the attention of researchers. The consensus mechanism at the center of blockchain is often criticized for wasting a large amount of computing power for meaningless hashing. At the same time, state-of-the-art models in deep learning require increasing computing power to be trained. Proof of Learning (PoL) is dedicated to using the originally wasted computing power to train neural networks. Most of the previous PoL consensus mechanisms are based on two methods, recomputation or performance metrics. However, in practical scenarios, these methods both do not satisfy all properties necessary to build a large-scale blockchain, such as certainty, constant verification, therefore are still far away from being practical. In this paper, we observe that the opacity of deep learning models is similar to the pre-image resistance of hash functions and can naturally be used to build PoL. Based on our observation, we propose a method called Directed Guiding Gradient. Using this method, our proposed PoL consensus mechanism has a similar structure to the widely used Proof of Work (PoW), allowing us to build practical blockchain on it and train neutral networks simultaneously. In experiments, we build a blockchain on top of our proposed PoL consensus mechanism and results show that our PoL works well.

Modern low-latency anonymity systems, no matter whether constructed as an overlay or implemented at the network layer, offer limited security guarantees against traffic analysis. On the other hand, high-latency anonymity systems offer strong security guarantees at the cost of computational overhead and long delays, which are excessive for interactive applications. We propose TARANET, an anonymity system that implements protection against traffic analysis at the network layer, and limits the incurred latency and overhead. In TARANET's setup phase, traffic analysis is thwarted by mixing. In the data transmission phase, end hosts and ASes coordinate to shape traffic into constant-rate transmission using packet splitting. Our prototype implementation shows that TARANET can forward anonymous traffic at over 50 Gbps using commodity hardware.

Abstract We identify two vulnerabilities for existing highspeed network-layer anonymity protocols, such as LAP and Dovetail. First, the header formats of LAP and Dovetail leak path information, reducing the anonymity-set size when an adversary launches topological attacks. Second, ASes can launch session hijacking attacks to deanonymize destinations. HORNET addresses these problems but incurs additional bandwidth overhead and latency. In this paper, we propose PHI, a Path-HIdden lightweight anonymity protocol that solves both challenges while maintaining the same level of efficiency as LAP and Dovetail. We present an efficient packet header format that hides path information and a new back-off setup method that is compatible with current and future network architectures. Our experiments demonstrate that PHI expands anonymity sets of LAP and Dovetail by over 30x and reaches 120 Gbps forwarding speed on a commodity software router.

We present HORNET, a system that enables high-speed end-to-end anonymous channels by leveraging next-generation network architectures. HORNET is designed as a low-latency onion routing system that operates at the network layer thus enabling a wide range of applications. Our system uses only symmetric cryptography for data forwarding yet requires no per-flow state on intermediate routers. This design enables HORNET routers implemented on off-the-shelf hardware to process anonymous traffic at over 93 Gb/s. HORNET is also highly scalable, adding minimal processing overhead per additional anonymous channel.

The flexibility of the current Domain Name System (DNS) has been stretched to its limits to accommodate new applications such as content delivery networks and dynamic DNS. In particular, maintaining cache consistency has become a much larger problem, as emerging technologies require increasingly-frequent updates to DNS records. Though Time-To-Live (TTL) is the most widely used method of controlling cache consistency, it does not offer the fine-grained control necessary for handling these frequent changes. In addition, TTLs are too static to handle sudden changes in traffic caused by Internet failures or social media trends, demonstrating their inflexibility in the face of unforeseen events. To address these problems, we first propose a metric called Expected Aggregate Inconsistency (EAI), which allows us to consider important factors such as a record's update frequency and popularity when quantitatively measuring inconsistency. We then design ECO-DNS, a lightweight system that leverages the information provided by EAI to optimize a record's TTL. This value can be tuned to individual cache servers' preferences between better consistency and bandwidth overhead. Further-more, our optimization model's flexibility allows us to easily adapt ECO-DNS to handle various caching hierarchies such as multi-level caching while considering the trade off among consistency, overhead, latency, and server load.

Outsourced computation services should ideally only charge customers for the resources used by their applications. Unfortunately, no verifiable basis for service providers and customers to reconcile resource accounting exists today. This leads to undesirable outcomes for both providers and consumers-providers cannot prove to customers that they really devoted the resources charged, and customers cannot verify that their invoice maps to their actual usage. As a result, many practical and theoretical attacks exist, aimed at charging customers for resources that their applications did not consume. Moreover, providers cannot charge consumers precisely, which causes them to bear the cost of unaccounted resources or pass these costs inefficiently to their customers.

Digital signatures are one of the fundamental security primitives in Vehicular Ad-Hoc Networks (VANETs) because they provide authenticity and non-repudiation in broadcast communication. However, the current broadcast authentication standard in VANETs is vulnerable to signature flooding: excessive signature verification requests that exhaust the computational resources of victims. In this paper, we propose two efficient broadcast authentication schemes, Fast Authentication (FastAuth) and Selective Authentication (SelAuth), as two countermeasures to signature flooding. FastAuth secures periodic single-hop beacon messages. By exploiting the sender's ability to predict its own future beacons, FastAuth enables 50 times faster verification than previous mechanisms using the Elliptic Curve Digital Signature Algorithm. SelAuth secures multi-hop applications in which a bogus signature may spread out quickly and impact a significant number of vehicles. SelAuth pro- vides fast isolation of malicious senders, even under a dynamic topology, while consuming only 15%--30% of the computational resources compared to other schemes. We provide both analytical and experimental evaluations based on real traffic traces and NS-2 simulations. With the near-term deployment plans of VANET on all vehicles, our approaches can make VANETs practical.