Biblio

Although the public cloud is known for its incredible capabilities, consumers cannot totally depend on cloud service providers to keep personal data because to the lack of client maneuverability. To protect privacy, data controllers outsourced encryption keys rather than providing information. Crypt - text to conduct out okay and founder access control and provide the encryption keys with others, innate quality Aes (CP-ABE) may be employed. This, however, falls short of effectively protecting against new dangers. The public cloud was unable to validate if a downloader could decode using a number of older methods. Therefore, these files should be accessible to everyone having access to a data storage. A malicious attacker may download hundreds of files in order to launch Economic Deny of Sustain (EDoS) attacks, greatly depleting the cloud resource. The user of cloud storage is responsible for paying the fee. Additionally, the public cloud serves as both the accountant and the payer of resource consumption costs, without offering data owners any information. Cloud infrastructure storage should assuage these concerns in practice. In this study, we provide a technique for resource accountability and defense against DoS attacks for encrypted cloud storage tanks. It uses black-box CP-ABE techniques and abides by the access policy of CP-arbitrary ABE. After presenting two methods for different parameters, speed and security evaluations are given.

A long-standing open question in computational learning theory is to prove NP-hardness of learning efficient programs, the setting of which is in between proper learning and improper learning. Ko (COLT’90, SICOMP’91) explicitly raised this open question and demonstrated its difficulty by proving that there exists no relativizing proof of NP-hardness of learning programs. In this paper, we overcome Ko’s relativization barrier and prove NP-hardness of learning programs under randomized polynomial-time many-one reductions. Our result is provably non-relativizing, and comes somewhat close to the parameter range of improper learning: We observe that mildly improving our inapproximability factor is sufficient to exclude Heuristica, i.e., show the equivalence between average-case and worst-case complexities of N P. We also make progress on another long-standing open question of showing NP-hardness of the Minimum Circuit Size Problem (MCSP). We prove NP-hardness of the partial function variant of MCSP as well as other meta-computational problems, such as the problems MKTP* and MINKT* of computing the time-bounded Kolmogorov complexity of a given partial string, under randomized polynomial-time reductions. Our proofs are algorithmic information (a.k. a. Kolmogorov complexity) theoretic. We utilize black-box pseudorandom generator constructions, such as the Nisan-Wigderson generator, as a one-time encryption scheme secure against a program which “does not know” a random function. Our key technical contribution is to quantify the “knowledge” of a program by using conditional Kolmogorov complexity and show that no small program can know many random functions.

Secured data transmission between one to many authorized users is achieved through Broadcast Encryption (BE). In BE, the source transmits encrypted data to multiple registered users who already have their decrypting keys. The Untrustworthy users, known as Traitors, can give out their secret keys to a hacker to form a pirate decoding system to decrypt the original message on the sly. The process of detecting the traitors is known as Traitor Tracing in cryptography. This paper presents a new Black Box Tracing method that is fully collusion resistant and it is designated as Traitor Tracing in Broadcast Encryption using Vector Keys (TTBE-VK). The proposed method uses integer vectors in the finite field Zp as encryption/decryption/tracing keys, reducing the computational cost compared to the existing methods.

Adversarial attacks have recently been proposed to scrutinize the security of deep neural networks. Most blackbox adversarial attacks, which have partial access to the target through queries, are target-specific; e.g., they require a well-trained surrogate that accurately mimics a given target. In contrast, target-agnostic black-box attacks are developed to attack any target; e.g., they learn a generalized surrogate that can adapt to any target via fine-tuning on samples queried from the target. Despite their success, current state-of-the-art target-agnostic attacks require tremendous fine-tuning steps and consequently an immense number of queries to the target to generate successful attacks. The high query complexity of these attacks makes them easily detectable and thus defendable. We propose a novel query-efficient target-agnostic attack that trains a generalized surrogate network to output the adversarial directions iv.r.t. the inputs and equip it with an effective fine-tuning strategy that only fine-tunes the surrogate when it fails to provide useful directions to generate the attacks. Particularly, we show that to effectively adapt to any target and generate successful attacks, it is sufficient to fine-tune the surrogate with informative samples that help the surrogate get out of the failure mode with additional information on the target’s local behavior. Extensive experiments on CIFAR10 and CIFAR-100 datasets demonstrate that the proposed target-agnostic approach can generate highly successful attacks for any target network with very few fine-tuning steps and thus significantly smaller number of queries (reduced by several order of magnitudes) compared to the state-of-the-art baselines.

Automatic speech recognition (ASR) models are used widely in applications for voice navigation and voice control of domestic appliances. ASRs have been misused by attackers to generate malicious outputs by attacking the deep learning component within ASRs. To assess the security and robustnesss of ASRs, we propose techniques within our framework SPAT that generate blackbox (agnostic to the DNN) adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox attacks that are time consuming and lack portability. Our techniques generate adversarial attacks that have no human audible difference by manipulating the input speech signal using a psychoacoustic model that maintains the audio perturbations below the thresholds of human perception. We propose a framework SPAT with three attack generation techniques based on the psychoacoustic concept and frame selection techniques to selectively target the attack. We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics- Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system. We found our adversarial attacks were portable across ASRs, not easily detected by a state-of the-art defense system, and had significant difference in output transcriptions while sounding similar to original audio.