Biblio

Unlike most social media, where automatic archiving of data is the default, Snapchat defaults to ephemerality: deleting content shortly after it is viewed by a receiver. Interviews with 25 Snapchat users show that ephemerality plays a key role in shaping their practices. Along with friend-adding features that facilitate a network of mostly close relations, default deletion affords everyday, mundane talk and reduces self-consciousness while encouraging playful interaction. Further, although receivers can save content through screenshots, senders are notified; this selective saving with notification supports complex information norms that preserve the feel of ephemeral communication while supporting the capture of meaningful content. This dance of giving and taking, sharing and showing, and agency for both senders and receivers provides the basis for a rich design space of mechanisms, levels, and domains for ephemerality.

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.

Call traces, i.e., sequences of function calls and returns, are fundamental to a wide range of program analyses such as bug reproduction, fault diagnosis, performance analysis, and many others. The conventional approach to collect call traces that instruments each function call and return site incurs large space and time overhead. Our approach aims at reducing the recording overheads by instrumenting only a small amount of call sites while keeping the capability of recovering the full trace. We propose a call trace model and a logged call trace model based on an LL(1) grammar, which enables us to define the criteria of a feasible solution to call trace collection. Based on the two models, we prove that to collect call traces with minimal instrumentation is an NP-hard problem. We then propose an efficient approach to obtaining a suboptimal solution. We implemented our approach as a tool Casper and evaluated it using the DaCapo benchmark suite. The experiment results show that our approach causes significantly lower runtime (and space) overhead than two state-of-the-arts approaches.

Named Data Networking (NDN), a clean-slate data oriented Internet architecture targeting on replacing IP, brings many potential benefits for content distribution. Real deployment of NDN is crucial to verify this new architecture and promote academic research, but work in this field is at an early stage. Due to the fundamental design paradigm difference between NDN and IP, Deploying NDN as IP overlay causes high overhead and inefficient transmission, typically in streaming applications. Aiming at achieving efficient NDN streaming distribution, this paper proposes a transitional architecture of NDN/IP hybrid network dubbed Centaur, which embodies both NDN's smartness, scalability and IP's transmission efficiency and deployment feasibility. In Centaur, the upper NDN module acts as the smart head while the lower IP module functions as the powerful feet. The head is intelligent in content retrieval and self-control, while the IP feet are able to transport large amount of media data faster than that if NDN directly overlaying on IP. To evaluate the performance of our proposal, we implement a real streaming prototype in ndnSIM and compare it with both NDN-Hippo and P2P under various experiment scenarios. The result shows that Centaur can achieve better load balance with lower overhead, which is close to the performance that ideal NDN can achieve. All of these validate that our proposal is a promising choice for the incremental and compatible deployment of NDN.

In the past decade, researchers have proposed various cloud storage integrity checking protocols to enable a cloud storage user to validate the integrity of the user's outsourced data. While the proposed solutions can in principle solve the cloud storage integrity checking problem, they are not sufficient for current cloud storage practices. In this position paper, we show the gaps between theoretical and practical cloud storage integrity checking solutions, through a categorization of existing solutions and an analysis of their underlying assumptions. To bridge the gap, we also call for practical cloud storage integrity checking solutions for three scenarios.

In the past decade, researchers have proposed various cloud storage integrity checking protocols to enable a cloud storage user to validate the integrity of the user's outsourced data. While the proposed solutions can in principle solve the cloud storage integrity checking problem, they are not sufficient for current cloud storage practices. In this position paper, we show the gaps between theoretical and practical cloud storage integrity checking solutions, through a categorization of existing solutions and an analysis of their underlying assumptions. To bridge the gap, we also call for practical cloud storage integrity checking solutions for three scenarios.

Compressive sensing is a new technique by which sparse signals are sampled and recovered from a few measurements. To address the disadvantages of traditional space image compressing methods, a complete new compressing scheme under the compressive sensing framework was developed in this paper. Firstly, in the coding stage, a simple binary measurement matrix was constructed to obtain signal measurements. Secondly, the input image was divided into small blocks. The image blocks then would be used as training sets to get a dictionary basis for sparse representation with learning algorithm. At last, sparse reconstruction algorithm was used to recover the original input image. Experimental results show that both the compressing rate and image recovering quality of the proposed method are high. Besides, as the computation cost is very low in the sampling stage, it is suitable for on-board applications in astronomy.

In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications). Many networked control systems employ Time-Triggered (TT) architectures that provide mechanisms enabling the exchange of precise and synchronous messages. TT systems have computation and communication constraints, and with the aim to enable secure communications in the network, it is important to evaluate the computational and communication overhead of implementing secure communication mechanisms. This paper presents a comprehensive analysis and evaluation of the effects of adding a Hash-based Message Authentication (HMAC) to TT networked control systems. The contributions of the paper include (1) the analysis and experimental validation of the communication overhead, as well as a scalability analysis that utilizes the experimental result for both wired and wireless platforms and (2) an experimental evaluation of the computational overhead of HMAC based on a kernel-level Linux implementation. An automotive application is used as an example, and the results show that it is feasible to implement a secure communication mechanism without interfering with the existing automotive controller execution times. The methods and results of the paper can be used for evaluating the performance impact of security mechanisms and, thus, for the design of secure wired and wireless TT networked control systems.

After a program has crashed and terminated abnormally, it typically leaves behind a snapshot of its crashing state in the form of a core dump. While a core dump carries a large amount of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory corruption vulnerabilities. A memory corruption vulnerability is a special type of software faults that an attacker can exploit to manipulate the content at a certain memory. As such, a core dump may contain a certain amount of corrupted data, which increases the difficulty in identifying useful debugging information (e.g. , a crash point and stack traces). Without a proper mechanism to deal with this problem, a core dump can be practically useless for software failure diagnosis. In this work, we develop CREDAL, an automatic tool that employs the source code of a crashing program to enhance core dump analysis and turns a core dump to an informative aid in tracking down memory corruption vulnerabilities. Specifically, CREDAL systematically analyzes a core dump potentially corrupted and identifies the crash point and stack frames. For a core dump carrying corrupted data, it goes beyond the crash point and stack trace. In particular, CREDAL further pinpoints the variables holding corrupted data using the source code of the crashing program along with the stack frames. To assist software developers (or security analysts) in tracking down a memory corruption vulnerability, CREDAL also performs analysis and highlights the code fragments corresponding to data corruption. To demonstrate the utility of CREDAL, we use it to analyze 80 crashes corresponding to 73 memory corruption vulnerabilities archived in Offensive Security Exploit Database. We show that, CREDAL can accurately pinpoint the crash point and (fully or partially) restore a stack trace even though a crashing program stack carries corrupted data. In addition, we demonstrate CREDAL can potentially reduce the manual effort of finding the code fragment that is likely to contain memory corruption vulnerabilities.

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect arm reaching by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of human movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID an commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios in both horizontal and vertical deployment modes. The experimental results show that APID can achieve high detection accuracy.

Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels. Existing hashing based methods generally project heterogeneous features to a common space for hash codes generation, and the supervising information is incrementally used for improving performance. However, these methods may produce ineffective hash codes, due to the failure to explore the discriminative property of supervising information and to effectively bridge the semantic gap between different modalities. To address these challenges, we propose a novel hashing based method in a linear classification framework, in which the proposed method learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective optimization algorithm is developed for the proposed method to jointly learn the modality-specific hash function, the unified binary codes and a linear classifier. Extensive experiments on three benchmark datasets highlight the advantage of the proposed method and show that it achieves the state-of-the-art performance.

Querying over encrypted data is gaining increasing popularity in cloud-based data hosting services. Security and efficiency are recognized as two important and yet conflicting requirements for querying over encrypted data. In this article, we propose an efficient private keyword search (EPKS) scheme that supports binary search and extend it to dynamic settings (called DEPKS) for inverted index–based encrypted data. First, we describe our approaches of constructing a searchable symmetric encryption (SSE) scheme that supports binary search. Second, we present a novel framework for EPKS and provide its formal security definitions in terms of plaintext privacy and predicate privacy by modifying Shen et al.’s security notions [Shen et al. 2009]. Third, built on the proposed framework, we design an EPKS scheme whose complexity is logarithmic in the number of keywords. The scheme is based on the groups of prime order and enjoys strong notions of security, namely statistical plaintext privacy and statistical predicate privacy. Fourth, we extend the EPKS scheme to support dynamic keyword and document updates. The extended scheme not only maintains the properties of logarithmic-time search efficiency and plaintext privacy and predicate privacy but also has fewer rounds of communications for updates compared to existing dynamic search encryption schemes. We experimentally evaluate the proposed EPKS and DEPKS schemes and show that they are significantly more efficient in terms of both keyword search complexity and communication complexity than existing randomized SSE schemes.

Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated the effectiveness of adapting prediction techniques in vulnerability detection, the feasibility of those techniques is limited mainly because of insufficient training data sets. This paper proposes a prediction technique targeting at early identification of potentially vulnerable software components. In the proposed scheme, the potentially vulnerable components are viewed as mislabeled data that may contain true but not yet observed vulnerabilities. The proposed hybrid technique combines the supports vector machine algorithm and ensemble learning strategy to better identify potential vulnerable components. The proposed vulnerability detection scheme is evaluated using some Java Android applications. The results demonstrated that the proposed hybrid technique could identify potentially vulnerable classes with high precision and relatively acceptable accuracy and recall.

While event logs generated by business processes play an increasingly significant role in business analysis, the quality of data remains a serious problem. Automatic recovery of dirty event logs is desirable and thus receives more attention. However, existing methods only focus on missing event recovery, or fall short of efficiency. To this end, we present Effa, a ProM plugin, to automatically recover event logs in the light of process specifications. Based on advanced heuristics including process decomposition and trace replaying to search the minimum recovery, Effa achieves a balance between repairing accuracy and efficiency.

In the cloud storage, users lose direct control over their data. How to surely delete data in the cloud becomes a crucial problem for a secure cloud storage system. The existing way to this problem is to encrypt the data before outsourcing and destroy the encryption key when deleting. However, this solution may cause heavy computation overhead for the user-side and the encrypted data remains intact in the cloud after the deletion operation. To solve this challenge problem, we propose a novel method to surely delete data in the cloud storage by overwriting. Different from existing works, our scheme is efficient in the user-side and is able to wipe out the deleted data from the drives of the cloud servers.