Biblio

While cloud-based deep learning benefits for high-accuracy inference, it leads to potential privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on exploring the feasibility of steganography in preserving inference privacy. Specifically, we devise GHOST and GHOST+, two private inference solutions employing steganography to make sensitive images invisible in the inference phase. Motivated by the fact that deep neural networks (DNNs) are inherently vulnerable to adversarial attacks, our main idea is turning this vulnerability into the weapon for data privacy, enabling the DNN to misclassify a stego image into the class of the sensitive image hidden in it. The main difference is that GHOST retrains the DNN into a poisoned network to learn the hidden features of sensitive images, but GHOST+ leverages a generative adversarial network (GAN) to produce adversarial perturbations without altering the DNN. For enhanced privacy and a better computation-communication trade-off, both solutions adopt the edge-cloud collaborative framework. Compared with the previous solutions, this is the first work that successfully integrates steganography and the nature of DNNs to achieve private inference while ensuring high accuracy. Extensive experiments validate that steganography has excellent ability in accuracy-aware privacy protection of deep learning.

Dynamic searchable symmetric encryption (DSSE) that enables a client to perform searches and updates on encrypted data has been intensively studied in cloud computing. Recently, forward privacy and backward privacy has engaged significant attention to protect DSSE from the leakage of updates. However, the research in this field almost focused on keyword-level updates. That is, the client needs to know the keywords of the documents in advance. In this paper, we proposed a document-level update scheme, DBP, which supports immediate deletion while guaranteeing forward privacy and backward privacy. Compared with existing forward and backward private DSSE schemes, our DBP scheme has the following merits: 1) Practicality. It achieves deletion based on document identifiers rather than document/keyword pairs; 2) Efficiency. It utilizes only lightweight primitives to realize backward privacy while supporting immediate deletion. Experimental evaluation on two real datasets demonstrates the practical efficiency of our scheme.

Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no information regarding the behavioral characteristics of malware. It also requires a large amount of training data and is prone to labeling difficulties and can reduce accuracy due to redundant training data. Therefore, we propose a malware detection method based on deep learning, which uses malware images and a set of autoencoders to detect malware. The method is to design an autoencoder to learn the functional characteristics of malware, and then to observe the reconstruction error of autoencoder to realize the classification and detection of malware and benign software. The proposed approach achieves 93% accuracy and comparatively better F1-score values while detecting malware and needs little training data when compared with traditional malware detection systems.

Due to environmental influence and technology limitation, a wireless sensor/sensors module can neither store or process all raw data locally nor reliably forward it to a destination in heterogeneous IoT environment. As a result, the data collected by the IoT's sensors are inherently noisy, unreliable, and may trigger many false alarms. These false or misleading data can lead to wrong decisions once the data reaches end entities. Therefore, it is highly recommended and desirable to acquire trustworthy data before data transmission, aggregation, and data storing at the end entities/cloud. In this paper, we propose an In-network Generalized Trustworthy Data Collection (IGTDC) framework for trustworthy data acquisition and faulty sensor detection in the IoT environment. The key idea of IGTDC is to allow a sensor's module to examine locally whether the raw data is trustworthy before transmitting towards upstream nodes. It further distinguishes whether the acquired data can be trusted or not before data aggregation at the sink/edge node. Besides, IGTDC helps to recognize a faulty or compromised sensor. For a reliable data collection, we use collaborative IoT technique, gate-level modeling, and programmable logic device (PLD) to ensure that the acquired data is reliable before transmitting towards upstream nodes/cloud. We use a hardware-based technique called “Gray Code” to detect a faulty sensor. Through simulations we reveal that the acquired data in IGTDC framework is reliable that can make a trustworthy data collection for event detection, and assist to distinguish a faulty sensor.

One critical challenge of today's cloud services is how to provide an effective search service while preserving user privacy. In this paper, we propose a wildcard-based multi-keyword fuzzy search (WMFS) scheme over the encrypted data, which tolerates keyword misspellings by exploiting the indecomposable property of primes. Compared with existing secure fuzzy search schemes, our WMFS scheme has the following merits: 1) Efficiency. It eliminates the requirement of a predefined dictionary and thus supports updates efficiently. 2) High accuracy. It eliminates the false positive and false negative introduced by specific data structures and thus allows the user to retrieve files as accurate as possible. 3) Flexibility. It gives the user great flexibility to specify different search patterns including keyword and substring matching. Extensive experiments on a real data set demonstrate the effectiveness and efficiency of our scheme.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.