Efficient Yet Robust Privacy for Video Streaming

MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. The standard has been widely utilized by the video streaming industry. However, it creates an opportunity for an adversary to invade users’ privacy. While a user is watching a video, information is leaked in the form of meta-data, the size and time that the server sent data to the user. This information is not protected by encryption and can be used to create a fingerprint for a video. Once the fingerprint is created, the adversary can use this to identify whether a target user is watching the corresponding video. Successful attack schemes have been proposed based on this leakage of user data using both Machine Learning (ML) and algorithmic approaches. Only one defense strategy has been proposed to deal with this problem: using differential privacy that adds a sufficient amount of noise in order to muddle the attacks. However, this strategy still suffers from the trade-off between the privacy level and efficiency for both the server and the client. To break through the problem, this paper proposes two schemes. A server-side defense and a client-side defense against the attacks with rigorous privacy and performance constraints, creating a totally private, scalable solution that outperforms the extant schemes. Our two proposed schemes, No Data are Alone (NDA) and a proposed scheme that uses only a single cluster (Single Cluster Solution), are developed based on KMeans clustering and are highly efficient. The experimental results show that our schemes are more than two times as efficient, in terms of excess downloaded video (represented as waste), than the most efficient differential privacy-based scheme. Additionally, no classifier can achieve an accuracy above 7.07% against videos obfuscated with our scheme NDA and 2.5% against our Single Cluster Solution

Rethinking the Weakness of Stream Ciphers and Its Application to Encrypted Malware Detection

Encryption key use is a critical component to the security of a stream cipher: because many implementations simply consist of a key scheduling algorithm and logical exclusive or (XOR), an attacker can completely break the cipher by XORing two ciphertexts encrypted under the same key, revealing the original plaintexts and the key itself. The research presented in this paper reinterprets this phenomenon, using repeated-key cryptanalysis for stream cipher identification. It has been found that a stream cipher executed under a fixed key generates patterns in each character of the ciphertexts it produces and that these patterns can be used to create a fingerprint which is distinct to a certain stream cipher and encryption key pair. A discrimination function, trained on this fingerprint, optimally separates ciphertexts generated through an enciphering pair from those which are generated by any other means. The patterns were observed in the Rivest Cipher 4 (RC4), ChaCha20-Poly1305, and Salsa20 stream ciphers as well as block cipher modes of operation that perform similarly to stream ciphers, such as: Counter (CTR), Galois/Counter (GCM), and Output feedback (OFB) modes. The discriminatory scheme proposed in this study perfectly detects ciphertexts of a fixed-key stream cipher with or without explicit knowledge of the key which may be utilized to detect a specific type of malware that exploits a stream cipher with a stored key to encrypt or obfuscate its activity. Finally, using real-world example of this type of malware, it is shown that the scheme is capable of detecting packets sent by the DarkComet remote access trojan, which utilizes RC4, with 100% accuracy in about 36 μs, providing a fast and highly accurate tool to aid in detecting malware using encryption

Digestive neural networks:A novel defense strategy against inference attacks in federated learning

Federated Learning (FL) is an efficient and secure machine learning technique designed for decentralized computing systems such as fog and edge computing. Its learning process employs frequent communications as the participating local devices send updates, either gradients or parameters of their models, to a central server that aggregates them and redistributes new weights to the devices. In FL, private data does not leave the individual local devices, and thus, rendered as a robust solution in terms of privacy preservation. However, the recently introduced membership inference attacks pose a critical threat to the impeccability of FL mechanisms. By eavesdropping only on the updates transferring to the center server, these attacks can recover the private data of a local device. A prevalent solution against such attacks is the differential privacy scheme that augments a sufficient amount of noise to each update to hinder the recovering process. However, it suffers from a significant sacrifice in the classification accuracy of the FL. To effectively alleviate the problem, this paper proposes a Digestive Neural Network (DNN), an independent neural network attached to the FL. The private data owned by each device will pass through the DNN and then train the FL. The DNN modifies the input data, which results in distorting updates, in a way to maximize the classification accuracy of FL while the accuracy of inference attacks is minimized. Our simulation result shows that the proposed DNN shows significant performance on both gradient sharing- and weight sharing-based FL mechanisms. For the gradient sharing, the DNN achieved higher classification accuracy by 16.17% while 9% lower attack accuracy than the existing differential privacy schemes. For the weight sharing FL scheme, the DNN achieved at most 46.68% lower attack success rate with 3% higher classification accuracy

A Federated Approach for Fine-Grained Classification of Fashion Apparel

As online retail services proliferate and are pervasive in modern lives, applications for classifying fashion apparel features from image data are becoming more indispensable. Online retailers, from leading companies to start-ups, can leverage such applications in order to increase profit margin and enhance the consumer experience. Many notable schemes have been proposed to classify fashion items, however, the majority of which focused upon classifying basic-level categories, such as T-shirts, pants, skirts, shoes, bags, and so forth. In contrast to most prior efforts, this paper aims to enable an in-depth classification of fashion item attributes within the same category. Beginning with a single dress, we seek to classify the type of dress hem, the hem length, and the sleeve length. The proposed scheme is comprised of three major stages: (a) localization of a target item from an input image using semantic segmentation, (b) detection of human key points (e.g., point of shoulder) using a pre-trained CNN and a bounding box, and (c) three phases to classify the attributes using a combination of algorithmic approaches and deep neural networks. The experimental results demonstrate that the proposed scheme is highly effective, with all categories having average precision of above 93.02%, and outperforms existing Convolutional Neural Networks (CNNs)-based schemes.Comment: 11 pages, 4 figures, 5 tables, submitted to IEEE ACCESS (under review

Rethinking the Weakness of Stream Ciphers and Its Application to Encrypted Malware Detection

One critical vulnerability of stream ciphers is the reuse of an encryption key. Since most stream ciphers consist of only a key scheduling algorithm and an Exclusive OR (XOR) operation, an adversary may break the cipher by XORing two captured ciphertexts generated under the same key. Various cryptanalysis techniques based on this property have been introduced in order to recover plaintexts or encryption keys; in contrast, this research reinterprets the vulnerability as a method of detecting stream ciphers from the ciphertexts it generates. Patterns found in the values (characters) expressed across the bytes of a ciphertext make the ciphertext distinguishable from random and are unique to each combination of ciphers and encryption keys. We propose a scheme that uses these patterns as a fingerprint, which is capable of detecting all ciphertexts of a given length generated by an encryption pair. The scheme can be utilized to detect a specific type of malware that exploits a stream cipher with a stored key such as the DarkComet Remote Access Trojan (RAT). We show that our scheme achieves 100%; accuracy for messages longer than 13 bytes in about 17 mu sec, providing a fast and highly accurate tool to aid in encrypted malware detection

Leveraging Smart Contracts for Asynchronous Group Key Agreement in Internet of Things

Group Key Agreement (GKA) mechanism plays a crucial role in the realization of various secure applications in various networks such as, but not limited to, sensor networks, Internet of Things (IoT), vehicular networks, social networks, and so on. To be suitable for IoT, GKA must satisfy several critical requirements. First, a GKA mechanism must be robust against a compromised device attack and satisfy essential secrecy definitions without the existence of a Trusted Third Party (TTP). TTP is often used by IoT devices in the establishment of ad hoc networks and usually, these devices are resource-constrained. Second, the GKA mechanism must be capable of distributing session keys successfully even with offline devices. Third, GKA must reduce the burden of heavy cryptographic computations for IoT devices. Based on these observations, in this paper, we propose a new GKA scheme that satisfies all the aforementioned requirements. The proposed scheme leverages smart contracts to alleviate the computational and storage overheads on the IoT devices induced by cryptographic functions. It also brings the advantage of asynchronism such that offline devices will be able to compute the group key once they are online since the essential information for obtaining the group key is stored inside the blockchain. We implement and test the proposed scheme on an Ethereum test network. The obtained results show that it consumes 5,264,150 gas to create a group, 994,178 gas to add a new member, and 798,431 gas to update a group key when the group has 20 members

Application of Artificial Intelligence in the Practice of Medicine

Advancements in artificial intelligence (AI) based on machine and deep learning are transforming certain medical disciplines [...

Application of Artificial Intelligence in the Practice of Medicine

Advancements in artificial intelligence (AI) based on machine and deep learning are transforming certain medical disciplines [...