Search

The purpose of writing this paper. Through the in-depth understanding and value analysis of Mo Gao Grottoe at Dunhuang culture, as well as the analysis of the design of daily furniture — —according to hangers, the comparison of product types and the current situation of development, this paper tries to do a good job in the study of integrating Dunhuang cultural symbols with furniture design. How to write this paper. The first is to study the development of Dunhuang culture and the types of murals represented by Mogao Grottoes culture through historical literature exploration and research, and the second is to investigate and analyze the clothes hangers and compare the existing clothes hangers. Through these two methods, the shortcomings of existing hangers are found and the necessity of innovative design of hangers is put forward. The third is to integrate Dunhuang culture with clothes hanger through semiotics in order to better spread Dunhuang culture and let more people understand the depth of Chinese traditional culture. Fourth, through the method of computer-aided design, the product modeling, the determination of the scale, color collocation, design deliberation, refining and finally according to the semiotics and formal beauty law to come up with a computer-aided scheme. Write the conclusion of this paper. According to the interpretation and analysis of Dunhuang Mogao Grottoes culture combined with the current situation of social shortcomings of clothes hangers, the two are combined by semiotics research method to help people understand the connotation of Chinese traditional culture in their daily life.

This paper describes the status and attention of artificial intelligence technology in the international academic and industrial circles, as well as the classification and appearance characteristics of wearable products. This paper analyzes the correlation between artificial intelligence technology and product applicability, and discusses the combination method of wearable products and artificial intelligence technology. Through the research on the appearance design of wearable products and the aesthetic analysis of wearable backpacks, an intelligent assisted wearable backpack with product aesthetic characteristics was designed by using design practice. The design not only meets the functional requirements of the product, but also meets the visual aesthetic requirements, which makes the design not only integrates artificial intelligence technology, but also conforms to formal aesthetics.

The smart grid frequently collects consumers’ fine-grained power usage data through smart meters to facilitate various applications, such as billing, load monitoring, regional statistics, and demand response. However, the smart meter reading streams may also pose severe privacy threats to the consumers by leaking their appliances’ ON/OFF status. In this paper, we first quantitatively measure the information leakage with respect to specific appliances’ status from any reading stream, and define a novel privacy notion to bound such information leakage. In addition, we propose a privacy preserving streaming algorithm with different options to effectively convert readings and promptly stream safe readings in different fashions. The output time series readings satisfy our privacy notion while guaranteeing excellent utility, such as extremely low aggregation errors and billing errors. Finally, we experimentally validate the effectiveness and efficiency of our approach using real data sets.

In recent years, with the use of mobile phone APPs, more and more people like to use tourism APPs when traveling, and the tourism industry also enters a new era of wireless tourism. College students are a huge tourist group. This paper will summarize an APP suitable for students’ tourism through the analysis of tourism APPs and tourist groups. This APP will make travel plans based on the student’s time, including travel time, travel residence, travel strategy, travel expenses, tour groups, etc. It also includes working with schools and local police to keep students safe. There should be such an APP for student travel to make it convenient for students to travel, enrich their university experience, and make student travel safer and more convenient.

Data owners are expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be to an acceptable level. Typically, a disclosure algorithm evaluates potential generalization functions in a predetermined order, and then discloses the first generalization that satisfies the desired privacy property. Recent studies show that adversarial inferences using knowledge about such disclosure algorithms can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k-jump algorithms. We then prove that the data utility of different k-jump algorithms is generally incomparable. The comparison of data utility is independent of utility measures and syntactic privacy models. Finally, we analyze the computational complexity of k-jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.

Web-based applications are gaining popularity as they require less client-side resources, and are easier to deliver and maintain. On the other hand, web applications also pose new security and privacy challenges. In particular, recent research revealed that many high profile web applications might cause sensitive user inputs to be leaked from encrypted traffic due to side-channel attacks exploiting unique patterns in packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, were shown to incur prohibitive overhead while still failing to guarantee sufficient privacy protection. In this paper, we first observe an interesting similarity between this privacy-preserving traffic padding (PPTP) issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we present a formal PPTP model encompassing the privacy requirements, padding costs, and padding methods. We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world web applications.

In this chapter, we present a formal Privacy-Preserving Traffic Padding (PPTP) model encompassing the privacy requirements, padding costs, and padding methods to prevent side-channel leaks due to unique patterns in packet sizes and directions of the encrypted traffic among components of the Web application. Web-based applications are gaining popularity as they require less client-side resources, and are easier to deliver and maintain. On the other hand, Web applications also pose new security and privacy challenges. In particular, recent research revealed that many high profile Web applications might cause sensitive user inputs to be leaked from encrypted traffic due to side-channel attacks exploiting unique patterns in packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, were shown to incur prohibitive overhead while still failing to guarantee sufficient privacy protection. In this chapter, we first observe an interesting similarity between this privacy-preserving traffic padding (PPTP) issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we present a formal PPTP model encompassing the privacy requirements, padding costs, and padding methods. We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world Web applications.

The solutions in the previous chapter rely on the assumption that adversaries do not possess prior background knowledge about possible user inputs, which is a common limitation shared by most existing solutions. In this chapter, we discuss a random ceiling padding approach whose results are resistant to such adversarial knowledge. Recent studies show that a Web-based application may be inherently vulnerable to side-channel attacks which exploit unique packet sizes to identify sensitive user inputs from encrypted traffic. Existing solutions based on packet padding or packet-size rounding generally rely on the assumption that adversaries do not possess prior background knowledge about possible user inputs. In this chapter, we propose a novel random ceiling padding approach whose results are resistant to such adversarial knowledge. Specifically, the approach injects randomness into the process of forming padding groups, such that an adversary armed with background knowledge would still face sufficient uncertainty in estimating user inputs. We formally present a generic scheme and discuss two concrete instantiations. We then confirm the correctness and performance of our approach through both theoretic analysis and experiments with two real world applications.

The previous chapters have described in-depth studies of side channel leaks in different applications. Although those side channel leaks and their corresponding countermeasures all look very different, there in fact exists some commonality in terms of both challenges and solutions. For example, in previous chapters, we usually apply a similar idea for tacking side channel leaks, i.e., we divide objects into groups and then break the linkage inside each group by obfuscating any observable information about the objects, where objects are either published micro-data records, encrypted packets (sizes) in web applications, or readings reported by a smart meter. In this chapter, we design a general framework for preserving privacy against side-channel leaks. We show how different problems may fit into this framework by revisiting the three side channel leaks covered in previous chapters.

In this chapter, we discuss how sensitive information about a household’s appliance status may be leaked from fine-grained smart meter readings. This is also an example of side channel leak because the readings are not supposed to serve as a channel for learning about individual appliances’ on/off status. While the features in smart grid, underpinned by the fine-grained usage information, provide significant benefits for both utility and customers, they also pose new security and privacy challenges. Existing solutions on privacy-preserving smart metering usually assume the readings to be sensitive and aim at protecting the readings through aggregation. In this chapter, we observe that the privacy issue in smart metering goes beyond the fine-grained readings themselves. That is, it may not be sufficient to simply focus on protecting such readings through aggregation or other techniques, without first understanding how such readings may lead to inferences of the truly sensitive information, that is, the appliance status. To address this issue, we present a formal model for privacy based on inferring appliance status from fine-grained meter readings.

While the strategy in previous chapter is theoretically superior to existing ones due to its independence of utility measures and privacy models, and its privacy guarantee under publicly-known algorithms, it incurs a high computational complexity. In this chapter, we study an efficient strategy for diversity preserving data publishing with publicly known algorithms (algorithms as side-channel). Our main observation is that a high computational complexity is usually incurred when an algorithm conflates the processes of privacy preservation and utility optimization. We then propose a novel privacy streamliner approach to decouple those two processes for improving algorithm efficiency. More specifically, we first identify a set of potential privacy-preserving solutions satisfying that an adversary’s knowledge about this set itself will not help him/her to violate the privacy property; we can then optimize utility within this set without worrying about privacy breaches since such an optimization is now simulatable by adversaries. To make our approach more concrete, we study it in the context of micro-data release with publicly known generalization algorithms. The analysis and experiments both confirm our algorithms to be more efficient than existing solutions.

The privacy preserving issues have received significant attentions in various domains. Various models and techniques have been proposed to achieve optimal privacy with minimal costs. However, side-channel leakages (such as, publicly-known algorithms of data publishing, observable traffic information in web application, fine-grained readings in smart metering) further complicate the process of privacy preservation. In this thesis, we make the first effort on investigating a general framework to model side-channel attacks across different domains and applying the framework to various categories of applications. In privacy-preserving data publishing with publicly-known algorithms, we first theoretically study a generic strategy independent of data utility measures and syntactic privacy properties. We then propose an efficient approach to preserving diversity. In privacy-preserving traffic padding in Web applications, we first propose a formal PPTP model to quantify the privacies and costs based on the key observation about the similarity between data publishing and traffic padding. We then introduce randomness into the previous solutions to provide background knowledge-resistant privacy guarantee. In privacy-preserving smart metering, we propose a light-weight approach to simultaneously preserving privacy on both billing and consumption aggregation based on the key observation about the privacy issue beyond the fine-grained readings.

Secure multi-party computation (SMC) allows multiple parties to jointly and securely compute a function while preserving the privacy of the involved parties. In this regard, homomorphic cryptosystems allow users to perform addition or multiplication operations on encrypted values without having to decrypt the input values. In this paper, we propose both a cryptographic and a non-cryptographic privacy-preserving protocols that allow one party to collaboratively compute a private polynomial function with at least two other parties using semantically secure cryptosystems. In this respect, the function to be evaluated is kept private to the party in question. In addition, we assume that there is no fully trusted party and that all parties are semi-honest. Moreover, we assume that there is no collusion between the different parties. We present the two protocols in question and report on the underlying performance through the presentation of our implementation results. The proposed protocols are relevant in many applications such as evidence sharing in multi-party forensic investigations, situation awareness through the secure sharing in distributed monitoring of plan execution, etc.

The use of sensors is of paramount importance for closing the feedback control loops that secure efficient and automated/autonomous operation of mobile robots in real-life applications. This paper reviews the development of sensing in welding robot and vision sensing for the weld line inspection. The robot requires several types of sensors for balancing and path planning. High accuracy and resolution of these sensors is of paramount importance for successful control of the robot. In this paper we review some of these sensors and evaluate their suitability for use in the robot. Various vision sensors and position sensors are reviewed and their advantages and disadvantages are discussed. These sensors have been incorporated and evaluated.

While enjoying the convenience of Software as a Service (SaaS), users are also at an increased risk of privacy breaches. Recent studies show that a Web-based application may be inherently vulnerable to side-channel attacks which exploit unique packet sizes to identify sensitive user inputs from encrypted traffic. Existing solutions based on packet padding or packet-size rounding generally rely on the assumption that adversaries do not possess prior background knowledge about possible user inputs. In this paper, we propose a novel random ceiling padding approach whose results are resistant to such adversarial knowledge. Specifically, the approach injects randomness into the process of forming padding groups, such that an adversary armed with background knowledge would still face sufficient uncertainty in estimating user inputs. We formally present a generic scheme and discuss two concrete instantiations. We then confirm the correctness and performance of our approach through both theoretic analysis and experiments with two real world applications.

In releasing data with sensitive information, a data owner usually has seemingly conflicting goals, including privacy preservation, utility optimization, and algorithm efficiency. In this paper, we observe that a high computational complexity is usually incurred when an algorithm conflates the processes of privacy preservation and utility optimization. We then propose a novel privacy streamliner approach to decouple those two processes for improving algorithm efficiency. More specifically, we first identify a set of potential privacy-preserving solutions satisfying that an adversary's knowledge about this set itself will not help him/her to violate the privacy property; we can then optimize utility within this set without worrying about privacy breaches since such an optimization is now simulatable by adversaries. To make our approach more concrete, we study it in the context of micro-data release with publicly known generalization algorithms. The analysis and experiments both confirm our algorithms to be more efficient than existing solutions.

While web-based applications are becoming increasingly ubiquitous, they also present new security and privacy challenges. In particular, recent research revealed that many high profile Web applications might cause private user information to leak from encrypted traffic due to side-channel attacks exploiting packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, are shown to incur prohibitive cost while still not ensuring sufficient privacy protection. In this paper, we propose a novel k-indistinguishable traffic padding technique to achieve the optimal tradeoff between privacy protection and communication and computational cost. Specifically, we first present a formal model of the privacy-preserving traffic padding (PPTP). We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world Web applications.

While web-based applications are gaining popularity, they also pose new security challenges. In particular, Chen et al. recently revealed that many popular Web applications actually leak out highly sensitive data from encrypted traffic due to side-channel attacks using packet sizes and timing [1]. They further demonstrated that existing solutions usually incur a high overhead while still not guaranteeing privacy protection. In this paper, we observe a striking similarity between this issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we propose a formal model for privacy-preserving traffic padding (PPTP) that encompasses privacy requirement, padding cost, and padding methods.

In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be limited to an acceptable level. Recent studies show that adversarial inferences using knowledge about a disclosure algorithm can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of distinct safe algorithms, namely, k-jump algorithms. We prove that the data utility of different k-jump algorithms is generally incomparable. Therefore, a secret choice can be made among all k-jump algorithms to eliminate adversarial inferences while improving the data utility of disclosed micro-data.