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Suksomboon, Kalika, Shen, Zhishu, Ueda, Kazuaki, Tagami, Atsushi.  2019.  C2P2: Content-Centric Privacy Platform for Privacy-Preserving Monitoring Services. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:252–261.
Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.
Sepulveda, J., Zankl, A., Mischke, O..  2017.  Cache attacks and countermeasures for NTRUEncrypt on MPSoCs: Post-quantum resistance for the IoT. 2017 30th IEEE International System-on-Chip Conference (SOCC). :120–125.

Public-key cryptography (PKC), widely used to protect communication in the Internet of Things (IoT), is the basis for establishing secured communication channels between multiple parties. The foreseeable breakthrough of quantum computers represents a risk for many PKC ecosystems. Almost all approaches in use today rely on the hardness of factoring large integers or computing (elliptic-curve) discrete logarithms. It is known that cryptography based on these problems can be broken in polynomial time by Shors algorithm, once a large enough quantum computer is built. In order to prepare for such an event, the integration of quantum-resistant cryptography on devices operating in the IoT is mandatory to achieve long-term security. Due to their limited resources, tight performance requirements and long-term life-cycles, this is especially challenging for Multi-Processor System-on-Chips (MPSoCs) operating in this context. At the same time, it must be provided that well-known implementation attacks, such as those targeting a cipher's execution time or its use of the processor cache, are inhibited, as they've successfully been used to attack cryptosystems in the pre-quantum era. Hence, this work presents an analysis of the security-critical polynomial multiplication routine within the NTRU algorithm and its susceptibility to timing and cache attacks. We also propose two different countermeasures to harden systems with or without caches against said attacks, and include the evaluation of the respective overheads. We demonstrate that security against timing and cache attacks can be achieved with reasonable overheads depending on the chosen parameters of NTRU.

Bender, Michael A., Demaine, Erik D., Ebrahimi, Roozbeh, Fineman, Jeremy T., Johnson, Rob, Lincoln, Andrea, Lynch, Jayson, McCauley, Samuel.  2016.  Cache-Adaptive Analysis. Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures. :135–144.
Memory efficiency and locality have substantial impact on the performance of programs, particularly when operating on large data sets. Thus, memory- or I/O-efficient algorithms have received significant attention both in theory and practice. The widespread deployment of multicore machines, however, brings new challenges. Specifically, since the memory (RAM) is shared across multiple processes, the effective memory-size allocated to each process fluctuates over time. This paper presents techniques for designing and analyzing algorithms in a cache-adaptive setting, where the RAM available to the algorithm changes over time. These techniques make analyzing algorithms in the cache-adaptive model almost as easy as in the external memory, or DAM model. Our techniques enable us to analyze a wide variety of algorithms — Master-Method-style algorithms, Akra-Bazzi-style algorithms, collections of mutually recursive algorithms, and algorithms, such as FFT, that break problems of size N into subproblems of size Theta(Nc). We demonstrate the effectiveness of these techniques by deriving several results: 1. We give a simple recipe for determining whether common divide-and-conquer cache-oblivious algorithms are optimally cache adaptive. 2. We show how to bound an algorithm's non-optimality. We give a tight analysis showing that a class of cache-oblivious algorithms is a logarithmic factor worse than optimal. 3. We show the generality of our techniques by analyzing the cache-oblivious FFT algorithm, which is not covered by the above theorems. Nonetheless, the same general techniques can show that it is at most O(loglog N) away from optimal in the cache adaptive setting, and that this bound is tight. These general theorems give concrete results about several algorithms that could not be analyzed using earlier techniques. For example, our results apply to Fast Fourier Transform, matrix multiplication, Jacobi Multipass Filter, and cache-oblivious dynamic-programming algorithms, such as Longest Common Subsequence and Edit Distance. Our results also give algorithm designers clear guidelines for creating optimally cache-adaptive algorithms.
Gulmezoglu, Berk, Eisenbarth, Thomas, Sunar, Berk.  2017.  Cache-Based Application Detection in the Cloud Using Machine Learning. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :288–300.

Cross-VM attacks have emerged as a major threat on commercial clouds. These attacks commonly exploit hardware level leakages on shared physical servers. A co-located machine can readily feel the presence of a co-located instance with a heavy computational load through performance degradation due to contention on shared resources. Shared cache architectures such as the last level cache (LLC) have become a popular leakage source to mount cross-VM attack. By exploiting LLC leakages, researchers have already shown that it is possible to recover fine grain information such as cryptographic keys from popular software libraries. This makes it essential to verify implementations that handle sensitive data across the many versions and numerous target platforms, a task too complicated, error prone and costly to be handled by human beings. Here we propose a machine learning based technique to classify applications according to their cache access profiles. We show that with minimal and simple manual processing steps feature vectors can be used to train models using support vector machines to classify the applications with a high degree of success. The profiling and training steps are completely automated and do not require any inspection or study of the code to be classified. In native execution, we achieve a successful classification rate as high as 98% (L1 cache) and 78$\backslash$% (LLC) over 40 benchmark applications in the Phoronix suite with mild training. In the cross-VM setting on the noisy Amazon EC2 the success rate drops to 60$\backslash$% for a suite of 25 applications. With this initial study we demonstrate that it is possible to train meaningful models to successfully predict applications running in co-located instances.

Kim, Taewoo, Thirumaraiselvan, Vidhyasagar, Jia, Jianfeng, Li, Chen.  2017.  Caching Geospatial Objects in Web Browsers. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. :92:1–92:4.

Map-based services are becoming increasingly important in many applications. These services often need to show geospatial objects (e.g., cities and parks) in Web browsers, and being able to retrieve such objects efficiently is critical to achieving a low response time for user queries. In this demonstration we present a browser-based caching technique to store and load geospatial objects on a map in a Web page. The technique employs a hierarchical structure to store and index polygons, and does intelligent prefetching and cache replacement by utilizing the information about the user's recent browser activities. We demonstrate the usage of the technique in an application called TwitterMap for visualizing more than 1 billion tweets in real time. We show its effectiveness by using different replacement policies. The technique is implemented as a general-purpose Javascript library, making it suitable for other applications as well.

M. Moradi, F. Qian, Q. Xu, Z. M. Mao, D. Bethea, M. K. Reiter.  2015.  "Caesar: high-speed and memory-efficient forwarding engine for future internet architecture". 2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS). :171-182.

In response to the critical challenges of the current Internet architecture and its protocols, a set of so-called clean slate designs has been proposed. Common among them is an addressing scheme that separates location and identity with self-certifying, flat and non-aggregatable address components. Each component is long, reaching a few kilobits, and would consume an amount of fast memory in data plane devices (e.g., routers) that is far beyond existing capacities. To address this challenge, we present Caesar, a high-speed and length-agnostic forwarding engine for future border routers, performing most of the lookups within three fast memory accesses. To compress forwarding states, Caesar constructs scalable and reliable Bloom filters in Ternary Content Addressable Memory (TCAM). To guarantee correctness, Caesar detects false positives at high speed and develops a blacklisting approach to handling them. In addition, we optimize our design by introducing a hashing scheme that reduces the number of hash computations from k to log(k) per lookup based on hash coding theory. We handle routing updates while keeping filters highly utilized in address removals. We perform extensive analysis and simulations using real traffic and routing traces to demonstrate the benefits of our design. Our evaluation shows that Caesar is more energy-efficient and less expensive (in terms of total cost) compared to optimized IPv6 TCAM-based solutions by up to 67% and 43% respectively. In addition, the total cost of our design is approximately the same for various address lengths.

Singi, Kapil, Kaulgud, Vikrant, Bose, R.P. Jagadeesh Chandra, Podder, Sanjay.  2019.  CAG: Compliance Adherence and Governance in Software Delivery Using Blockchain. 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB). :32—39.

The software development life cycle (SDLC) starts with business and functional specifications signed with a client. In addition to this, the specifications also capture policy / procedure / contractual / regulatory / legislation / standard compliances with respect to a given client industry. The SDLC must adhere to service level agreements (SLAs) while being compliant to development activities, processes, tools, frameworks, and reuse of open-source software components. In today's world, global software development happens across geographically distributed (autonomous) teams consuming extraordinary amounts of open source components drawn from a variety of disparate sources. Although this is helping organizations deal with technical and economic challenges, it is also increasing unintended risks, e.g., use of a non-complaint license software might lead to copyright issues and litigations, use of a library with vulnerabilities pose security risks etc. Mitigation of such risks and remedial measures is a challenge due to lack of visibility and transparency of activities across these distributed teams as they mostly operate in silos. We believe a unified model that non-invasively monitors and analyzes the activities of distributed teams will help a long way in building software that adhere to various compliances. In this paper, we propose a decentralized CAG - Compliance Adherence and Governance framework using blockchain technologies. Our framework (i) enables the capturing of required data points based on compliance specifications, (ii) analyzes the events for non-conformant behavior through smart contracts, (iii) provides real-time alerts, and (iv) records and maintains an immutable audit trail of various activities.

Xiao-Bing Hu, Ming Wang, Leeson, M.S..  2014.  Calculating the complete pareto front for a special class of continuous multi-objective optimization problems. Evolutionary Computation (CEC), 2014 IEEE Congress on. :290-297.

Existing methods for multi-objective optimization usually provide only an approximation of a Pareto front, and there is little theoretical guarantee of finding the real Pareto front. This paper is concerned with the possibility of fully determining the true Pareto front for those continuous multi-objective optimization problems for which there are a finite number of local optima in terms of each single objective function and there is an effective method to find all such local optima. To this end, some generalized theoretical conditions are firstly given to guarantee a complete cover of the actual Pareto front for both discrete and continuous problems. Then based on such conditions, an effective search procedure inspired by the rising sea level phenomenon is proposed particularly for continuous problems of the concerned class. Even for general continuous problems to which not all local optima are available, the new method may still work well to approximate the true Pareto front. The good practicability of the proposed method is especially underpinned by multi-optima evolutionary algorithms. The advantages of the proposed method in terms of both solution quality and computational efficiency are illustrated by the simulation results.

Jia, Kaige, Liu, Zheyu, Wei, Qi, Qiao, Fei, Liu, Xinjun, Yang, Yi, Fan, Hua, Yang, Huazhong.  2018.  Calibrating Process Variation at System Level with In-Situ Low-Precision Transfer Learning for Analog Neural Network Processors. Proceedings of the 55th Annual Design Automation Conference. :12:1–12:6.

Process Variation (PV) may cause accuracy loss of the analog neural network (ANN) processors, and make it hard to be scaled down, as well as feasibility degrading. This paper first analyses the impact of PV on the performance of ANN chips. Then proposes an in-situ transfer learning method at system level to reduce PV's influence with low-precision back-propagation. Simulation results show the proposed method could increase 50% tolerance of operating point drift and 70% $\sim$ 100% tolerance of mismatch with less than 1% accuracy loss of benchmarks. It also reduces 66.7% memories and has about 50× energy-efficiency improvement of multiplication in the learning stage, compared with the conventional full-precision (32bit float) training system.

Wang, Kai, Zhang, Yuqing, Liu, Peng.  2016.  Call Me Back!: Attacks on System Server and System Apps in Android Through Synchronous Callback. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :92–103.

Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.

Zhang, Zhikun, Wang, Tianhao, Li, Ninghui, He, Shibo, Chen, Jiming.  2018.  CALM: Consistent Adaptive Local Marginal for Marginal Release Under Local Differential Privacy. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :212–229.
Marginal tables are the workhorse of capturing the correlations among a set of attributes. We consider the problem of constructing marginal tables given a set of user's multi-dimensional data while satisfying Local Differential Privacy (LDP), a privacy notion that protects individual user's privacy without relying on a trusted third party. Existing works on this problem perform poorly in the high-dimensional setting; even worse, some incur very expensive computational overhead. In this paper, we propose CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. More importantly, CALM can scale well with large data dimensions and marginal sizes. We conduct extensive experiments on several real world datasets. Experimental results demonstrate the effectiveness and efficiency of CALM over existing methods.
Srivastava, Animesh, Jain, Puneet, Demetriou, Soteris, Cox, Landon P., Kim, Kyu-Han.  2017.  CamForensics: Understanding Visual Privacy Leaks in the Wild. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. :30:1–30:13.

Many mobile apps, including augmented-reality games, bar-code readers, and document scanners, digitize information from the physical world by applying computer-vision algorithms to live camera data. However, because camera permissions for existing mobile operating systems are coarse (i.e., an app may access a camera's entire view or none of it), users are vulnerable to visual privacy leaks. An app violates visual privacy if it extracts information from camera data in unexpected ways. For example, a user might be surprised to find that an augmented-reality makeup app extracts text from the camera's view in addition to detecting faces. This paper presents results from the first large-scale study of visual privacy leaks in the wild. We build CamForensics to identify the kind of information that apps extract from camera data. Our extensive user surveys determine what kind of information users expected an app to extract. Finally, our results show that camera apps frequently defy users' expectations based on their descriptions.

Zhuo Lu, Wenye Wang, Wang, C..  2015.  Camouflage Traffic: Minimizing Message Delay for Smart Grid Applications under Jamming. Dependable and Secure Computing, IEEE Transactions on. 12:31-44.

Smart grid is a cyber-physical system that integrates power infrastructures with information technologies. To facilitate efficient information exchange, wireless networks have been proposed to be widely used in the smart grid. However, the jamming attack that constantly broadcasts radio interference is a primary security threat to prevent the deployment of wireless networks in the smart grid. Hence, spread spectrum systems, which provide jamming resilience via multiple frequency and code channels, must be adapted to the smart grid for secure wireless communications, while at the same time providing latency guarantee for control messages. An open question is how to minimize message delay for timely smart grid communication under any potential jamming attack. To address this issue, we provide a paradigm shift from the case-by-case methodology, which is widely used in existing works to investigate well-adopted attack models, to the worst-case methodology, which offers delay performance guarantee for smart grid applications under any attack. We first define a generic jamming process that characterizes a wide range of existing attack models. Then, we show that in all strategies under the generic process, the worst-case message delay is a U-shaped function of network traffic load. This indicates that, interestingly, increasing a fair amount of traffic can in fact improve the worst-case delay performance. As a result, we demonstrate a lightweight yet promising system, transmitting adaptive camouflage traffic (TACT), to combat jamming attacks. TACT minimizes the message delay by generating extra traffic called camouflage to balance the network load at the optimum. Experiments show that TACT can decrease the probability that a message is not delivered on time in order of magnitude.

Anwar, Z., Malik, A.W..  2014.  Can a DDoS Attack Meltdown My Data Center? A Simulation Study and Defense Strategies Communications Letters, IEEE. 18:1175-1178.

The goal of this letter is to explore the extent to which the vulnerabilities plaguing the Internet, particularly susceptibility to distributed denial-of-service (DDoS) attacks, impact the Cloud. DDoS has been known to disrupt Cloud services, but could it do worse by permanently damaging server and switch hardware? Services are hosted in data centers with thousands of servers generating large amounts of heat. Heating, ventilation, and air-conditioning (HVAC) systems prevent server downtime due to overheating. These are remotely managed using network management protocols that are susceptible to network attacks. Recently, Cloud providers have experienced outages due to HVAC malfunctions. Our contributions include a network simulation to study the feasibility of such an attack motivated by our experiences of such a security incident in a real data center. It demonstrates how a network simulator can study the interplay of the communication and thermal properties of a network and help prevent the Cloud provider's worst nightmare: meltdown of the data center as a result of a DDoS attack.

Alan, Hasan Faik, Kaur, Jasleen.  2016.  Can Android Applications Be Identified Using Only TCP/IP Headers of Their Launch Time Traffic? Proceedings of the 9th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :61–66.

The ability to identify mobile apps in network traffic has significant implications in many domains, including traffic management, malware detection, and maintaining user privacy. App identification methods in the literature typically use deep packet inspection (DPI) and analyze HTTP headers to extract app fingerprints. However, these methods cannot be used if HTTP traffic is encrypted. We investigate whether Android apps can be identified from their launch-time network traffic using only TCP/IP headers. We first capture network traffic of 86,109 app launches by repeatedly running 1,595 apps on 4 distinct Android devices. We then use supervised learning methods used previously in the web page identification literature, to identify the apps that generated the traffic. We find that: (i) popular Android apps can be identified with 88% accuracy, by using the packet sizes of the first 64 packets they generate, when the learning methods are trained and tested on the data collected from same device; (ii) when the data from an unseen device (but similar operating system/vendor) is used for testing, the apps can be identified with 67% accuracy; (iii) the app identification accuracy does not drop significantly even if the training data are stale by several days, and (iv) the accuracy does drop quite significantly if the operating system/vendor is very different. We discuss the implications of our findings as well as open issues.

Shayan, Mohammed, Bhattacharjee, Sukanta, Song, Yong-Ak, Chakrabarty, Krishnendu, Karri, Ramesh.  2019.  Can Multi-Layer Microfluidic Design Methods Aid Bio-Intellectual Property Protection? 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS). :151—154.
Researchers develop bioassays by rigorously experimenting in the lab. This involves significant fiscal and skilled person-hour investment. A competitor can reverse engineer a bioassay implementation by imaging or taking a video of a biochip when in use. Thus, there is a need to protect the intellectual property (IP) rights of the bioassay developer. We introduce a novel 3D multilayer-based obfuscation to protect a biochip against reverse engineering.
Rumez, Marcel, Dürrwang, Jürgen, Brecht, Tim, Steinshorn, Timo, Neugebauer, Peter, Kriesten, Reiner, Sax, Eric.  2019.  CAN Radar: Sensing Physical Devices in CAN Networks based on Time Domain Reflectometry. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.
Ling, Mee Hong, Yau, Kok-Lim Alvin.  2019.  Can Reinforcement Learning Address Security Issues? an Investigation into a Clustering Scheme in Distributed Cognitive Radio Networks 2019 International Conference on Information Networking (ICOIN). :296—300.
This paper investigates the effectiveness of reinforcement learning (RL) model in clustering as an approach to achieve higher network scalability in distributed cognitive radio networks. Specifically, it analyzes the effects of RL parameters, namely the learning rate and discount factor in a volatile environment, which consists of member nodes (or secondary users) that launch attacks with various probabilities of attack. The clusterhead, which resides in an operating region (environment) that is characterized by the probability of attacks, countermeasures the malicious SUs by leveraging on a RL model. Simulation results have shown that in a volatile operating environment, the RL model with learning rate α= 1 provides the highest network scalability when the probability of attacks ranges between 0.3 and 0.7, while the discount factor γ does not play a significant role in learning in an operating environment that is volatile due to attacks.
Schneeberger, Tanja, Scholtes, Mirella, Hilpert, Bernhard, Langer, Markus, Gebhard, Patrick.  2019.  Can Social Agents elicit Shame as Humans do? 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). :164–170.
This paper presents a study that examines whether social agents can elicit the social emotion shame as humans do. For that, we use job interviews, which are highly evaluative situations per se. We vary the interview style (shame-eliciting vs. neutral) and the job interviewer (human vs. social agent). Our dependent variables include observational data regarding the social signals of shame and shame regulation as well as self-assessment questionnaires regarding the felt uneasiness and discomfort in the situation. Our results indicate that social agents can elicit shame to the same amount as humans. This gives insights about the impact of social agents on users and the emotional connection between them.
Ali, M. A. M., Tahir, N. M..  2018.  Cancelable Biometrics Technique for Iris Recognition. 2018 IEEE Symposium on Computer Applications Industrial Electronics (ISCAIE). :434-437.

Iris recognition is one of the most reliable biometrics for identification purpose in terms of reliability and accuracy. Hence, in this research the integration of cancelable biometrics features for iris recognition using encryption and decryption non-invertible transformation is proposed. Here, the biometric data is protected via the proposed cancelable biometrics method. The experimental results showed that the recognition rate achieved is 99.9% using Bath-A dataset with a maximum decision criterion of 0.97 along with acceptable processing time.

Ferres, E., Immler, V., Utz, A., Stanitzki, A., Lerch, R., Kokozinski, R..  2018.  Capacitive Multi-Channel Security Sensor IC for Tamper-Resistant Enclosures. 2018 IEEE SENSORS. :1–4.
Physical attacks are a serious threat for embedded devices. Since these attacks are based on physical interaction, sensing technology is a key aspect in detecting them. For highest security levels devices in need of protection are placed into tamper-resistant enclosures. In this paper we present a capacitive multi-channel security sensor IC in a 350 nm CMOS technology. This IC measures more than 128 capacitive sensor nodes of such an enclosure with an SNR of 94.6 dB across a 16×16 electrode matrix in just 19.7 ms. The theoretical sensitivity is 35 aF which is practically limited by noise to 460 aF. While this is similar to capacitive touch technology, it outperforms available solutions of this domain with respect to precision and speed.
Le Métayer, Daniel, Rauzy, Pablo.  2018.  Capacity: An Abstract Model of Control over Personal Data. Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy. :64-75.

While the control of individuals over their personal data is increasingly seen as an essential component of their privacy, the word "control" is usually used in a very vague way, both by lawyers and by computer scientists. This lack of precision may lead to misunderstandings and makes it difficult to check compliance. To address this issue, we propose a formal framework based on capacities to specify the notion of control over personal data and to reason about control properties. We illustrate our framework with social network systems and show that it makes it possible to characterize the types of control over personal data that they provide to their users and to compare them in a rigorous way.

Shi, Wenxiao, Zhang, Ruidong, Ouyang, Min, Wang, Jihong.  2017.  The Capacity of Hybrid Wireless Mesh Network. Proceedings of the 3rd International Conference on Communication and Information Processing. :332–338.

Wireless mesh network (WMN) consists of mesh gateways, mesh routers and mesh clients. In hybrid WMN, both backbone mesh network and client mesh network are mesh connected. Capacity analysis of multi-hop wireless networks has proven to be an interesting and challenging research topic. The capacity of hybrid WMN depends on several factors such as traffic model, topology, scheduling strategy and bandwidth allocation strategy, etc. In this paper, the capacity of hybrid WMN is studied according to the traffic model and bandwidth allocation. The traffic of hybrid WMN is categorized into internal and external traffic. Then the capacity of each mesh client is deduced according to appropriate bandwidth allocation. The analytical results show that hybrid WMN achieves lower capacity than infrastructure WMN. The results and conclusions can guide for the construction of hybrid WMN.

Bartolini, Davide B., Miedl, Philipp, Thiele, Lothar.  2016.  On the Capacity of Thermal Covert Channels in Multicores. Proceedings of the Eleventh European Conference on Computer Systems. :24:1–24:16.

Modern multicore processors feature easily accessible temperature sensors that provide useful information for dynamic thermal management. These sensors were recently shown to be a potential security threat, since otherwise isolated applications can exploit them to establish a thermal covert channel and leak restricted information. Previous research showed experiments that document the feasibility of (low-rate) communication over this channel, but did not further analyze its fundamental characteristics. For this reason, the important questions of quantifying the channel capacity and achievable rates remain unanswered. To address these questions, we devise and exploit a new methodology that leverages both theoretical results from information theory and experimental data to study these thermal covert channels on modern multicores. We use spectral techniques to analyze data from two representative platforms and estimate the capacity of the channels from a source application to temperature sensors on the same or different cores. We estimate the capacity to be in the order of 300 bits per second (bps) for the same-core channel, i.e., when reading the temperature on the same core where the source application runs, and in the order of 50 bps for the 1-hop channel, i.e., when reading the temperature of the core physically next to the one where the source application runs. Moreover, we show a communication scheme that achieves rates of more than 45 bps on the same-core channel and more than 5 bps on the 1-hop channel, with less than 1% error probability. The highest rate shown in previous work was 1.33 bps on the 1-hop channel with 11% error probability.

Reijers, Niels, Shih, Chi-Sheng.  2018.  CapeVM: A Safe and Fast Virtual Machine for Resource-Constrained Internet-of-Things Devices. Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. :250-263.

This paper presents CapeVM, a sensor node virtual machine aimed at delivering both high performance and a sandboxed execution environment that ensures malicious code cannot corrupt the VM's internal state or perform actions not allowed by the VM. CapeVM uses Ahead-of-Time compilation and introduces a range of optimisations to eliminate most of the overhead present in previous work on sensor node AOT compilers. A sandboxed execution environment is guaranteed by a set of checks. The structured nature of the VM's instruction set allows the VM to perform most checks at load time, reducing the need for expensive run-time checks compared to native code approaches. While some overhead from using a VM and adding sandbox checks cannot be avoided, CapeVM's optimisations reduce this overhead dramatically. We evaluate CapeVM using a set of IoT applications and show this results in a performance just 2.1x slower than unsandboxed native code. Thus, CapeVM combines the desirable properties ofexisting work on both sandboxed execution and virtual machines for sensor nodes, with significantly improved performance.