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.

In recent years, the commodities industry has grown rapidly under the stimulus of domestic demand and the expansion of cross-border trade. It has also been combined with the rapid development of e-commerce technology in the same period to form a flexible and efficient e-commerce system for bulk commodities. However, the hasty combination of both has inspired a lack of effective regulatory measures in the bulk industry, leading to constant industry chaos. Among them, the problem of lagging evidence in regulatory platforms is particularly prominent. Based on this, we design a blockchain-based evidential and secure bulk-commodity supervisory system (abbr. BeBus). Setting different privacy protection policies for each participant in the system, the solution ensures effective forensics and tamper-proof evidence to meet the needs of the bulk business scenario.

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.

In the Bitcoin mining network, miners contribute computation power to solve crypto-puzzles in exchange for financial rewards. Due to the randomness and the competitiveness of mining, individual miners tend to join mining pools for low risks and steady incomes. Usually, a pool is managed by its central operator, who charges fees for providing risk-sharing services. This paper presents a hierarchical distributed computation paradigm where miners can distribute their power among multiple pools. By adding virtual pools, we separate miners’ dual roles of being the operator as well as being the member when solo mining. We formulate a multi-leader multi-follower Stackelberg game to study the joint utility maximization of pool operators and miners, thereby addressing a computation power allocation problem. We investigate two practical pool operation modes, a uniform-share-difficulty mode and a nonuniform-share-difficulty mode. We derive analytical results for the Stackelberg equilibrium of the game under both modes, based on which optimal strategies are designed for all operators and miners. Numerical evaluations are presented to verify the proposed model.

Voice-based input is usually used as the primary input method for augmented reality (AR) headsets due to immersive AR experience and good recognition performance. However, recent researches have shown that an attacker can inject inaudible voice commands to the devices that lack voice verification. Even if we secure voice input with voice verification techniques, an attacker can easily steal the victim's voice using low-cast handy recorders and replay it to voice-based applications. To defend against voice-spoofing attacks, AR headsets should be able to determine whether the voice is from the person who is using the AR headsets. Existing voice-spoofing defense systems are designed for smartphone platforms. Due to the special locations of microphones and loudspeakers on AR headsets, existing solutions are hard to be implemented on AR headsets. To address this challenge, in this paper, we propose a voice-spoofing defense system for AR headsets by leveraging both the internal body propagation and the air propagation of human voices. Experimental results show that our system can successfully accept normal users with average accuracy of 97% and defend against two types of attacks with average accuracy of at least 98%.

Smartwatches are expected to become the world's best-selling electronic product after smartphones. Various smart-watches have been released to the private consumer market, but the data on smartwatches is not well protected. In this paper, we show for the first time that photoplethysmography (PPG)signals influenced by hand gestures can be used to authenticate users on smartwatches. The insight is that muscle and tendon movements caused by hand gestures compress the arterial geometry with different degrees, which has a significant impact on the blood flow. Based on this insight, novel approaches are proposed to detect the starting point and ending point of the hand gesture from raw PPG signals and determine if these PPG signals are from a normal user or an attacker. Different from existing solutions, our approach leverages the PPG sensors that are available on most smartwatches and does not need to collect training data from attackers. Also, our system can be used in more general scenarios wherever users can perform hand gestures and is robust against shoulder surfing attacks. We conduct various experiments to evaluate the performance of our system and show that our system achieves an average authentication accuracy of 96.31 % and an average true rejection rate of at least 91.64% against two types of attacks.

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.

The ability to transmit high volumes of data over a long distance makes WiFi mesh networks an ideal transmission solution for remote video surveillance. Instead of independently manipulating the node deployment, channel and interface assignment, and routing to improve the network performance, we propose a joint network design using multi-objective genetic algorithm to take into account the interplay of them. Moreover, we found a performance evaluation method based on the transmission capability of the WiFi mesh networks for the first time. The good agreement of our obtained multiple optimized solutions to the extensive simulation results by NS-3 demonstrates the effectiveness of our design.

Smart grid, managed by intelligent devices, have demonstrated great potentials to help residential customers to optimally schedule and manage the appliances' energy consumption. Due to the fine-grained power consumption information collected by smart meter, the customers' privacy becomes a serious concern. Combined with the effects of fake guideline electricity price, this paper focuses an on-line appliance scheduling design to protect customers' privacy in a cost-effective way, while taking into account the influences of non-schedulable appliances' operation uncertainties. We formulate the problem by minimizing the expected sum of electricity cost and achieving acceptable privacy protection. Without knowledge of future electricity consumptions, an on-line scheduling algorithm is proposed based on the only current observations by using a stochastic dynamic programming technique. The simulation results demonstrate the effectiveness of the proposed algorithm using real-world data.