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Obster, M., Kowalewski, S..  2017.  A live static code analysis architecture for PLC software. 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). :1–4.

Static code analysis is a convenient technique to support the development of software. Without prior test setup, information about a later runtime behavior can be inferred and errors in the code can be found before using a regular compiler. Solutions to apply static code analysis to PLC software following the IEC 61131-3 already exist, but using these separate tools usually creates a gap in the development process. In this paper we introduce an architecture to use static analysis directly in a development environment and give instant feedback to the developer while he is still editing the PLC software.

Huang, Wei, Huang, Zhen, Miyani, Dhaval, Lie, David.  2016.  LMP: Light-weighted Memory Protection with Hardware Assistance. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :460–470.

Despite a long history and numerous proposed defenses, memory corruption attacks are still viable. A secure and low-overhead defense against return-oriented programming (ROP) continues to elude the security community. Currently proposed solutions still must choose between either not fully protecting critical data and relying instead on information hiding, or using incomplete, coarse-grain checking that can be circumvented by a suitably skilled attacker. In this paper, we present a light-weighted memory protection approach (LMP) that uses Intel's MPX hardware extensions to provide complete, fast ROP protection without having to rely in information hiding. We demonstrate a prototype that defeats ROP attacks while incurring an average runtime overhead of 3.9%.

Peng Li, Song Guo.  2014.  Load balancing for privacy-preserving access to big data in cloud. Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on. :524-528.

In the era of big data, many users and companies start to move their data to cloud storage to simplify data management and reduce data maintenance cost. However, security and privacy issues become major concerns because third-party cloud service providers are not always trusty. Although data contents can be protected by encryption, the access patterns that contain important information are still exposed to clouds or malicious attackers. In this paper, we apply the ORAM algorithm to enable privacy-preserving access to big data that are deployed in distributed file systems built upon hundreds or thousands of servers in a single or multiple geo-distributed cloud sites. Since the ORAM algorithm would lead to serious access load unbalance among storage servers, we study a data placement problem to achieve a load balanced storage system with improved availability and responsiveness. Due to the NP-hardness of this problem, we propose a low-complexity algorithm that can deal with large-scale problem size with respect to big data. Extensive simulations are conducted to show that our proposed algorithm finds results close to the optimal solution, and significantly outperforms a random data placement algorithm.

Chin, J., Zufferey, T., Shyti, E., Hug, G..  2019.  Load Forecasting of Privacy-Aware Consumers. 2019 IEEE Milan PowerTech. :1—6.

The roll-out of smart meters (SMs) in the electric grid has enabled data-driven grid management and planning techniques. SM data can be used together with short-term load forecasts (STLFs) to overcome polling frequency constraints for better grid management. However, the use of SMs that report consumption data at high spatial and temporal resolutions entails consumer privacy risks, motivating work in protecting consumer privacy. The impact of privacy protection schemes on STLF accuracy is not well studied, especially for smaller aggregations of consumers, whose load profiles are subject to more volatility and are, thus, harder to predict. In this paper, we analyse the impact of two user demand shaping privacy protection schemes, model-distribution predictive control (MDPC) and load-levelling, on STLF accuracy. Support vector regression is used to predict the load profiles at different consumer aggregation levels. Results indicate that, while the MDPC algorithm marginally affects forecast accuracy for smaller consumer aggregations, this diminishes at higher aggregation levels. More importantly, the load-levelling scheme significantly improves STLF accuracy as it smoothens out the grid visible consumer load profile.

Liu, Xinghua, Bai, Dandan, Jiang, Rui.  2020.  Load Frequency Control of Multi-area Power Systems under Deception Attacks*. 2020 Chinese Automation Congress (CAC). :3851–3856.
This paper investigated the sliding mode load frequency control (LFC) for an multi-area power system (MPS) under deception attacks (DA). A Luenberger observer is designed to obtain the state estimate of MPS. By using the Lyapunov-Krasovskii method, a sliding mode surface (SMS) is designed to ensure the stability. Then the accessibility analysis ensures that the trajectory of the MPS can reach the specified SMS. Finally, the serviceability of the method is explained by providing a case study.
Wang, Xi, Yao, Jun, Ji, Hongxia, Zhang, Ze, Li, Chen, Ma, Beizhi.  2018.  A Local Integral Hash Nearest Neighbor Algorithm. 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :544—548.

Nearest neighbor search algorithm plays a very important role in computer image algorithm. When the search data is large, we need to use fast search algorithm. The current fast retrieval algorithms are tree based algorithms. The efficiency of the tree algorithm decreases sharply with the increase of the data dimension. In this paper, a local integral hash nearest neighbor algorithm of the spatial space is proposed to construct the tree structure by changing the way of the node of the access tree. It is able to express data distribution characteristics. After experimental testing, this paper achieves more efficient performance in high dimensional data.

Ou, Yifan, Deng, Bin, Liu, Xuan, Zhou, Ke.  2019.  Local Outlier Factor Based False Data Detection in Power Systems. 2019 IEEE Sustainable Power and Energy Conference (iSPEC). :2003—2007.
The rapid developments of smart grids provide multiple benefits to the delivery of electric power, but at the same time makes the power grids under the threat of cyber attackers. The transmitted data could be deliberately modified without triggering the alarm of bad data detection procedure. In order to ensure the stable operation of the power systems, it is extremely significant to develop effective abnormal detection algorithms against injected false data. In this paper, we introduce the density-based LOF algorithm to detect the false data and dummy data. The simulation results show that the traditional density-clustering based LOF algorithm can effectively identify FDA, but the detection performance on DDA is not satisfactory. Therefore, we propose the improved LOF algorithm to detect DDA by setting reasonable density threshold.
Chatzopoulos, Dimitris, Gujar, Sujit, Faltings, Boi, Hui, Pan.  2016.  LocalCoin: An Ad-hoc Payment Scheme for Areas with High Connectivity: Poster. Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. :365–366.

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain.

Guang, Xuan, Yeung, Raymond w..  2019.  Local-Encoding-Preserving Secure Network Coding for Fixed Dimension. 2019 IEEE International Symposium on Information Theory (ISIT). :201-205.

In the paradigm of network coding, information-theoretic security is considered in the presence of wiretappers, who can access one arbitrary edge subset up to a certain size, referred to as the security level. Secure network coding is applied to prevent the leakage of the source information to the wiretappers. In this paper, we consider the problem of secure network coding for flexible pairs of information rate and security level with any fixed dimension (equal to the sum of rate and security level). We present a novel approach for designing a secure linear network code (SLNC) such that the same SLNC can be applied for all the rate and security-level pairs with the fixed dimension. We further develop a polynomial-time algorithm for efficient implementation and prove that there is no penalty on the required field size for the existence of SLNCs in terms of the best known lower bound by Guang and Yeung. Finally, by applying our approach as a crucial building block, we can construct a family of SLNCs that not only can be applied to all possible pairs of rate and security level but also share a common local encoding kernel at each intermediate node in the network.

Tan, R., Khan, N., Guan, L..  2020.  Locality Guided Neural Networks for Explainable Artificial Intelligence. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network (LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy.
Fang, Bo, Hua, Zhongyun, Huang, Hejiao.  2019.  Locality-Sensitive Hashing Scheme Based on Heap Sort of Hash Bucket. 2019 14th International Conference on Computer Science Education (ICCSE). :5–10.
Nearest neighbor search (NNS) is one of the current popular research directions, which widely used in machine learning, pattern recognition, image detection and so on. In the low dimension data, based on tree search method can get good results. But when the data dimension goes up, that will produce a curse of dimensional. The proposed Locality-Sensitive Hashing algorithm (LSH) greatly improves the efficiency of nearest neighbor query for high dimensional data. But the algorithm relies on the building a large number of hash table, which makes the space complexity very high. C2LSH based on dynamic collision improves the disadvantage of LSH, but its disadvantage is that it needs to detect the collision times of a large number of data points which Increased query time. Therefore, Based on LSH algorithm, later researchers put forward many improved algorithms, but still not ideal.In this paper, we put forward Locality-Sensitive Hashing Scheme Based on Heap Sort of Hash Bucket (HSLSH) algorithm aiming at the shortcomings of LSH and C2LSH. Its main idea is to take advantage of the efficiency of heapsort in massive data sorting to improve the efficiency of nearest neighbor query. It only needs to rely on a small number of hash functions can not only overcome the shortcoming of LSH need to build a large number of hash table, and avoids defects of C2LSH. Experiments show that our algorithm is more than 20% better than C2LSH in query accuracy and 40% percent lower in query time.
Jackson, K. A., Bennett, B. T..  2018.  Locating SQL Injection Vulnerabilities in Java Byte Code Using Natural Language Techniques. SoutheastCon 2018. :1-5.

With so much our daily lives relying on digital devices like personal computers and cell phones, there is a growing demand for code that not only functions properly, but is secure and keeps user data safe. However, ensuring this is not such an easy task, and many developers do not have the required skills or resources to ensure their code is secure. Many code analysis tools have been written to find vulnerabilities in newly developed code, but this technology tends to produce many false positives, and is still not able to identify all of the problems. Other methods of finding software vulnerabilities automatically are required. This proof-of-concept study applied natural language processing on Java byte code to locate SQL injection vulnerabilities in a Java program. Preliminary findings show that, due to the high number of terms in the dataset, using singular decision trees will not produce a suitable model for locating SQL injection vulnerabilities, while random forest structures proved more promising. Still, further work is needed to determine the best classification tool.

Altay, Osman, Ulas, Mustafa.  2018.  Location Determination by Processing Signal Strength of Wi-Fi Routers in the Indoor Environment with Linear Discriminant Classifier. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1-4.

Location determination in the indoor areas as well as in open areas is important for many applications. But location determination in the indoor areas is a very difficult process compared to open areas. The Global Positioning System (GPS) signals used for position detection is not effective in the indoor areas. Wi-Fi signals are a widely used method for localization detection in the indoor area. In the indoor areas, localization can be used for many different purposes, such as intelligent home systems, locations of people, locations of products in the depot. In this study, it was tried to determine localization for with the classification method for 4 different areas by using Wi-Fi signal values obtained from different routers for indoor location determination. Linear discriminant analysis (LDA) classification was used for classification. In the test using 10k fold cross-validation, 97.2% accuracy value was calculated.

Dagelić, Ante, Perković, Toni, Čagalj, Mario.  2019.  Location Privacy and Changes in WiFi Probe Request Based Connection Protocols Usage Through Years. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech). :1–5.
Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.
Catania, E., Corte, A. La.  2018.  Location Privacy in Virtual Cell-Equipped Ultra-Dense Networks. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–4.

Ultra-dense Networks are attracting significant interest due to their ability to provide the next generation 5G cellular networks with a high data rate, low delay, and seamless coverage. Several factors, such as interferences, energy constraints, and backhaul bottlenecks may limit wireless networks densification. In this paper, we study the effect of mobile node densification, access node densification, and their aggregation into virtual entities, referred to as virtual cells, on location privacy. Simulations show that the number of tracked mobile nodes might be statistically reduced up to 10 percent by implementing virtual cells. Moreover, experiments highlight that success of tracking attacks has an inverse relationship to the number of moving nodes. The present paper is a preliminary attempt to analyse the effectiveness of cell virtualization to mitigate location privacy threats in ultra-dense networks.

Feng, Tianyi, Wong, Wai-Choong, Sun, Sumei, Zhao, Yonghao, Zhang, Zhixiang.  2019.  Location Privacy Preservation and Location-based Service Quality Tradeoff Framework Based on Differential Privacy. 2019 16th Workshop on Positioning, Navigation and Communications (WPNC). :1–6.
With the widespread use of location-based services and the development of localization systems, user's locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users' location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users' true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.
Petrov, D., Znati, T..  2017.  Location privacy preserving protocols in database-enabled cognitive radio networks. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). :147–152.

The exponential growth in the number of mobile devices, combined with the rapid demand for wireless services, has steadily stressed the wireless spectrum, calling for new techniques to improve spectrum utilization. A geo-location database has been proposed as a viable solution for wireless users to determine spectrum availability in cognitive radio networks. The protocol used by secondary users (SU) to request spectral availability for a specific location, time and duration, may reveal confidential information about these users. In this paper, we focus on SUs' location privacy in database-enabled wireless networks and propose a framework to address this threat. The basic tenet of the framework is obfuscation, whereby channel requests for valid locations are interwoven with requests for fake locations. Traffic redirection is also used to deliberately confuse potential query monitors from inferring users' location information. Within this framework, we propose two privacy-preserving schemes. The Master Device Enabled Location Privacy Preserving scheme utilizes trusted master devices to prevent leaking information of SUs' locations to attackers. The Crowd Sourced Location Privacy Preserving scheme builds a guided tour of randomly selected volunteers to deliver users channel availability queries and ensure location privacy. Security analysis and computational and communication overhead of these schemes are discussed.

Bai, Leqiang, Li, Guoku.  2018.  Location Privacy Protection of WSN Based on Network Partition and Angle. 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). :1254–1260.
For the phantom routing algorithm, phantom source nodes are concentrated near the real source node, and for the angle based phantom routing algorithm, phantom source nodes focus on some areas, and the existing source location privacy protection algorithm has low security cycle, a source location privacy protection algorithm of wireless sensor networks based on angle and network partition is proposed. The algorithm selects the next hop node on forwarding path according to the angle relationship between neighbors, and ensures that phantom source nodes are far away from the real source node and have the diversity of geographic location through network partition. Simulation results show that, compared with the existing source location privacy protection algorithm, this algorithm can induce attackers to deviate from the real path, and increase security cycle.
Kita, Kentaro, Kurihara, Yoshiki, Koizumi, Yuki, Hasegawa, Toru.  2018.  Location Privacy Protection with a Semi-honest Anonymizer in Information Centric Networking. Proceedings of the 5th ACM Conference on Information-Centric Networking. :95–105.
Location-based services, which provide services based on locations of consumers' interests, are becoming essential for our daily lives. Since the location of a consumer's interest contains private information, several studies propose location privacy protection mechanisms using an anonymizer, which sends queries specifying anonymous location sets, each of which contains k - 1 locations in addition to a location of a consumer's interest, to an LBS provider based on the k-anonymity principle. The anonymizer is, however, assumed to be trusted/honest, and hence it is a single point of failure in terms of privacy leakage. To address this privacy issue, this paper designs a semi-honest anonymizer to protect location privacy in NDN networks. This study first reveals that session anonymity and location anonymity must be achieved to protect location privacy with a semi-honest anonymizer. Session anonymity is to hide who specifies which anonymous location set and location anonymity is to hide a location of a consumer's interest in a crowd of locations. We next design an architecture to achieve session anonymity and an algorithm to generate anonymous location sets achieving location anonymity. Our evaluations show that the architecture incurs marginal overhead to achieve session anonymity and anonymous location sets generated by the algorithm sufficiently achieve location anonymity.
Shi, W., Liu, S., Zhang, J., Zhang, R..  2020.  A Location-aware Computation Offloading Policy for MEC-assisted Wireless Mesh Network. 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops). :53–58.
Mobile edge computing (MEC), an emerging technology, has the characteristics of low latency, mobile energy savings, and context-awareness. As a type of access network, wireless mesh network (WMN) has gained wide attention due to its flexible network architecture, low deployment cost, and self-organization. The combination of MEC and WMN can solve the shortcomings of traditional wireless communication such as storage capacity, privacy, and security. In this paper, we propose a location-aware (LA) algorithm to cognize the location and a location-aware offloading policy (LAOP) algorithm considering the energy consumption and time delay. Simulation results show that the proposed LAOP algorithm can obtain a higher completion rate and lower average processing delay compared with the other two methods.
Hintze, Daniel, Koch, Eckhard, Scholz, Sebastian, Mayrhofer, René.  2016.  Location-based Risk Assessment for Mobile Authentication. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. :85–88.

Mobile devices offer access to our digital lives and thus need to be protected against the risk of unauthorized physical access by applying strong authentication, which in turn adversely affects usability. The actual risk, however, depends on dynamic factors like day and time. In this paper we discuss the idea of using location-based risk assessment in combination with multi-modal biometrics to adjust the level of authentication necessary to the situational risk of unauthorized access.

Novikova, Evgenia, Bekeneva, Yana, Shorov, Andrey.  2019.  The Location-Centric Approach to Employee's Interaction Pattern Detection. 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). :373–378.
The task of the insider threat detection is one of the most sophisticated problems of the information security. The analysis of the logs of the access control system may reveal on how employees move and interact providing thus better understanding on how personnel observe security policies and established business processes. The paper presents an approach to the detection of the location-centric employees' interaction patterns. The authors propose the formal definition of the interaction patterns and present the visualization-driven technique to the extraction of the patterns from the data when any prior information about existing interaction routine and procedures is not available. The proposed approach is demonstrated on the data set provided within VAST MiniChallenge-2 2016 contest.
Joy, Joshua, Le, Minh, Gerla, Mario.  2016.  LocationSafe: Granular Location Privacy for IoT Devices. Proceedings of the Eighth Wireless of the Students, by the Students, and for the Students Workshop. :39–41.

Today, mobile data owners lack consent and control over the release and utilization of their location data. Third party applications continuously process and access location data without data owners granular control and without knowledge of how location data is being used. The proliferation of GPS enabled IoT devices will lead to larger scale abuses of trust. In this paper we present the first design and implementation of a privacy module built into the GPSD daemon. The GPSD daemon is a low-level GPS interface that runs on GPS enabled devices. The integration of the privacy module ensures that data owners have granular control over the release of their GPS location. We describe the design of our privacy module integration into the GPSD daemon.

Bloom, Gedare, Parmer, Gabriel, Simha, Rahul.  2016.  LockDown: An Operating System for Achieving Service Continuity by Quarantining Principals. Proceedings of the 9th European Workshop on System Security. :7:1–7:6.

This paper introduces quarantine, a new security primitive for an operating system to use in order to protect information and isolate malicious behavior. Quarantine's core feature is the ability to fork a protection domain on-the-fly to isolate a specific principal's execution of untrusted code without risk of a compromise spreading. Forking enables the OS to ensure service continuity by permitting even high-risk operations to proceed, albeit subject to greater scrutiny and constraints. Quarantine even partitions executing threads that share resources into isolated protection domains. We discuss the design and implementation of quarantine within the LockDown OS, a security-focused evolution of the Composite component-based microkernel OS. Initial performance results for quarantine show that about 98% of the overhead comes from the cost of copying memory to the new protection domain.

Chin, Paul, Cao, Yuan, Zhao, Xiaojin, Zhang, Leilei, Zhang, Fan.  2019.  Locking Secret Data in the Vault Leveraging Fuzzy PUFs. 2019 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :1–6.

Physical Unclonable Functions (PUFs) are considered as an attractive low-cost security anchor. The unique features of PUFs are dependent on the Nanoscale variations introduced during the manufacturing variations. Most PUFs exhibit an unreliability problem due to aging and inherent sensitivity to the environmental conditions. As a remedy to the reliability issue, helper data algorithms are used in practice. A helper data algorithm generates and stores the helper data in the enrollment phase in a secure environment. The generated helper data are used then for error correction, which can transform the unique feature of PUFs into a reproducible key. The key can be used to encrypt secret data in the security scheme. In contrast, this work shows that the fuzzy PUFs can be used to secret important data directly by an error-tolerant protocol without the enrollment phase and error-correction algorithm. In our proposal, the secret data is locked in a vault leveraging the unique fuzzy pattern of PUF. Although the noise exists, the data can then be released only by this unique PUF. The evaluation was performed on the most prominent intrinsic PUF - DRAM PUF. The test results demonstrate that our proposal can reach an acceptable reconstruction rate in various environment. Finally, the security analysis of the new proposal is discussed.