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2021-06-24
Moran, Kevin, Palacio, David N., Bernal-Cárdenas, Carlos, McCrystal, Daniel, Poshyvanyk, Denys, Shenefiel, Chris, Johnson, Jeff.  2020.  Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :873—885.
Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually establishing trace links, researchers have developed automated approaches that draw relationships between pairs of textual software artifacts using similarity measures. However, the effectiveness of such techniques are often limited as they only utilize a single measure of artifact similarity and cannot simultaneously model (implicit and explicit) relationships across groups of diverse development artifacts. In this paper, we illustrate how these limitations can be overcome through the use of a tailored probabilistic model. To this end, we design and implement a HierarchiCal PrObabilistic Model for SoftwarE Traceability (Comet) that is able to infer candidate trace links. Comet is capable of modeling relationships between artifacts by combining the complementary observational prowess of multiple measures of textual similarity. Additionally, our model can holistically incorporate information from a diverse set of sources, including developer feedback and transitive (often implicit) relationships among groups of software artifacts, to improve inference accuracy. We conduct a comprehensive empirical evaluation of Comet that illustrates an improvement over a set of optimally configured baselines of ≈14% in the best case and ≈5% across all subjects in terms of average precision. The comparative effectiveness of Comet in practice, where optimal configuration is typically not possible, is likely to be higher. Finally, we illustrate Comet's potential for practical applicability in a survey with developers from Cisco Systems who used a prototype Comet Jenkins plugin.
2021-06-02
Gohari, Parham, Hale, Matthew, Topcu, Ufuk.  2020.  Privacy-Preserving Policy Synthesis in Markov Decision Processes. 2020 59th IEEE Conference on Decision and Control (CDC). :6266—6271.
In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation's investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protects the privacy of the transition probabilities in a Markov decision process. We use differential privacy as the mathematical definition of privacy. The algorithm first perturbs the transition probabilities using a mechanism that provides differential privacy. Then, based on the privatized transition probabilities, we synthesize a policy using dynamic programming. Our main contribution is to bound the "cost of privacy," i.e., the difference between the expected total rewards with privacy and the expected total rewards without privacy. We also show that computing the cost of privacy has time complexity that is polynomial in the parameters of the problem. Moreover, we establish that the cost of privacy increases with the strength of differential privacy protections, and we quantify this increase. Finally, numerical experiments on two example environments validate the established relationship between the cost of privacy and the strength of data privacy protections.
Sun, Mingjing, Zhao, Chengcheng, He, Jianping.  2020.  Privacy-Preserving Correlated Data Publication with a Noise Adding Mechanism. 2020 IEEE 16th International Conference on Control Automation (ICCA). :494—499.
The privacy issue in data publication is critical and has been extensively studied. However, most of the existing works assume the data to be published is independent, i.e., the correlation among data is neglected. The correlation is unavoidable in data publication, which universally manifests intrinsic correlations owing to social, behavioral, and genetic relationships. In this paper, we investigate the privacy concern of data publication where deterministic and probabilistic correlations are considered, respectively. Specifically, (ε,δ)-multi-dimensional data-privacy (MDDP) is proposed to quantify the correlated data privacy. It characterizes the disclosure probability of the published data being jointly estimated with the correlation under a given accuracy. Then, we explore the effects of deterministic correlations on privacy disclosure. For deterministic correlations, it is shown that the successful disclosure rate with correlations increases compared to the one without knowing the correlation. Meanwhile, a closed-form solution of the optimal disclosure probability and the strict bound of privacy disclosure gain are derived. Extensive simulations on a real dataset verify our analytical results.
2021-06-01
Patnaikuni, Shrinivasan, Gengaje, Sachin.  2020.  Properness and Consistency of Syntactico-Semantic Reasoning using PCFG and MEBN. 2020 International Conference on Communication and Signal Processing (ICCSP). :0554–0557.
The paper proposes a formal approach for parsing grammatical derivations in the context of the principle of semantic compositionality by defining a mapping between Probabilistic Context Free Grammar (PCFG) and Multi Entity Bayesian Network (MEBN) theory, which is a first-order logic for modelling probabilistic knowledge bases. The principle of semantic compositionality states that meaning of compound expressions is dependent on meanings of constituent expressions forming the compound expression. Typical pattern analysis applications focus on syntactic patterns ignoring semantic patterns governing the domain in which pattern analysis is attempted. The paper introduces the concepts and terminologies of the mapping between PCFG and MEBN theory. Further the paper outlines a modified version of CYK parser algorithm for parsing PCFG derivations driven by MEBN. Using Kullback- Leibler divergence an outline for proving properness and consistency of the PCFG mapped with MEBN is discussed.
2021-05-25
Nazemi, Mostafa, Dehghanian, Payman, Alhazmi, Mohannad, Wang, Fei.  2020.  Multivariate Uncertainty Characterization for Resilience Planning in Electric Power Systems. 2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I CPS). :1—8.
Following substantial advancements in stochastic classes of decision-making optimization problems, scenario-based stochastic optimization, robust\textbackslashtextbackslash distributionally robust optimization, and chance-constrained optimization have recently gained an increasing attention. Despite the remarkable developments in probabilistic forecast of uncertainties (e.g., in renewable energies), most approaches are still being employed in a univariate framework which fails to unlock a full understanding on the underlying interdependence among uncertain variables of interest. In order to yield cost-optimal solutions with predefined probabilistic guarantees, conditional and dynamic interdependence in uncertainty forecasts should be accommodated in power systems decision-making. This becomes even more important during the emergencies where high-impact low-probability (HILP) disasters result in remarkable fluctuations in the uncertain variables. In order to model the interdependence correlation structure between different sources of uncertainty in power systems during both normal and emergency operating conditions, this paper aims to bridge the gap between the probabilistic forecasting methods and advanced optimization paradigms; in particular, perdition regions are generated in the form of ellipsoids with probabilistic guarantees. We employ a modified Khachiyan's algorithm to compute the minimum volume enclosing ellipsoids (MVEE). Application results based on two datasets on wind and photovoltaic power are used to verify the efficiency of the proposed framework.
Ramasubramanian, Bhaskar, Niu, Luyao, Clark, Andrew, Bushnell, Linda, Poovendran, Radha.  2020.  Privacy-Preserving Resilience of Cyber-Physical Systems to Adversaries. 2020 59th IEEE Conference on Decision and Control (CDC). :3785–3792.

A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.

2021-03-29
Volkov, A. I., Semin, V. G., Khakimullin, E. R..  2020.  Modeling the Structures of Threats to Information Security Risks based on a Fuzzy Approach. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT QM IS). :132—135.

The article deals with the development and implementation of a method for synthesizing structures of threats and risks to information security based on a fuzzy approach. We consider a method for modeling threat structures based on structural abstractions: aggregation, generalization, and Association. It is shown that the considered forms of structural abstractions allow implementing the processes of Ascending and Descending inheritance. characteristics of the threats. A database of fuzzy rules based on procedural abstractions has been developed and implemented in the fuzzy logic tool environment Fussy Logic.

2021-03-22
OGISO, S., Mohri, M., Shiraishi, Y..  2020.  Transparent Provable Data Possession Scheme for Cloud Storage. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–5.
Provable Data Possession (PDP) is one of the data security techniques to make sure that the data stored in the cloud storage exists. In PDP, the integrity of the data stored in the cloud storage is probabilistically verified by the user or a third-party auditor. In the conventional PDP, the user creates the metadata used for audition. From the viewpoint of user convenience, it is desirable to be able to audit without operations other than uploading. In other words, the challenge is to provide a transparent PDP that verifies the integrity of files according to the general cloud storage system model so as not to add operations to users. We propose a scheme in which the cloud generates the metadata used during verification, and the user only uploads files. It is shown that the proposed scheme is resistant to the forgery of cloud proof and the acquisition of data by a third-party auditor.
2021-03-17
Haseeb, J., Mansoori, M., Welch, I..  2020.  A Measurement Study of IoT-Based Attacks Using IoT Kill Chain. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :557—567.

Manufacturing limitations, configuration and maintenance flaws associated with the Internet of Things (IoT) devices have resulted in an ever-expanding attack surface. Attackers exploit IoT devices to steal private information, take part in botnets, perform Denial of Service (DoS) attacks and use their resources for the mining of cryptocurrency. In this paper, we experimentally evaluate a hypothesis that attacks on IoT devices follow the generalised Cyber Kill Chain (CKC) model. We used a medium-interaction honeypot to capture and analyse more than 30,000 attacks targeting IoT devices. We classified the steps taken by the attackers using the CKC model and extended CKC to an IoT Kill Chain (IoTKC) model. The IoTKC provides details about IoT-specific attack characteristics and attackers' activities in the exploitation of IoT devices.

2021-03-09
Xiao, Y., Zhang, N., Lou, W., Hou, Y. T..  2020.  Modeling the Impact of Network Connectivity on Consensus Security of Proof-of-Work Blockchain. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :1648—1657.

Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.

2020-12-17
Wehbe, R., Williams, R. K..  2019.  Approximate Probabilistic Security for Networked Multi-Robot Systems. 2019 International Conference on Robotics and Automation (ICRA). :1997—2003.

In this paper, we formulate a combinatorial optimization problem that aims to maximize the accuracy of a lower bound estimate of the probability of security of a multi-robot system (MRS), while minimizing the computational complexity involved in its calculation. Security of an MRS is defined using the well-known control theoretic notion of left invertiblility, and the probability of security of an MRS can be calculated using binary decision diagrams (BDDs). The complexity of a BDD depends on the number of disjoint path sets considered during its construction. Taking into account all possible disjoint paths results in an exact probability of security, however, selecting an optimal subset of disjoint paths leads to a good estimate of the probability while significantly reducing computation. To deal with the dynamic nature of MRSs, we introduce two methods: (1) multi-point optimization, a technique that requires some a priori knowledge of the topology of the MRS over time, and (2) online optimization, a technique that does not require a priori knowledge, but must construct BDDs while the MRS is operating. Finally, our approach is validated on an MRS performing a rendezvous objective while exchanging information according to a noisy state agreement process.

2020-12-14
Pilet, A. B., Frey, D., Taïani, F..  2020.  Foiling Sybils with HAPS in Permissionless Systems: An Address-based Peer Sampling Service. 2020 IEEE Symposium on Computers and Communications (ISCC). :1–6.
Blockchains and distributed ledgers have brought renewed interest in Byzantine fault-tolerant protocols and decentralized systems, two domains studied for several decades. Recent promising works have in particular proposed to use epidemic protocols to overcome the limitations of popular Blockchain mechanisms, such as proof-of-stake or proof-of-work. These works unfortunately assume a perfect peer-sampling service, immune to malicious attacks, a property that is difficult and costly to achieve. We revisit this fundamental problem in this paper, and propose a novel Byzantine-tolerant peer-sampling service that is resilient to Sybil attacks in open systems by exploiting the underlying structure of wide-area networks.
2020-12-07
Allig, C., Leinmüller, T., Mittal, P., Wanielik, G..  2019.  Trustworthiness Estimation of Entities within Collective Perception. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The idea behind collective perception is to improve vehicles' awareness about their surroundings. Every vehicle shares information describing its perceived environment by means of V2X communication. Similar to other information shared using V2X communication, collective perception information is potentially safety relevant, which means there is a need to assess the reliability and quality of received information before further processing. Transmitted information may have been forged by attackers or contain inconsistencies e.g. caused by malfunctions. This paper introduces a novel approach for estimating a belief that a pair of entities, e.g. two remote vehicles or the host vehicle and a remote vehicle, within a Vehicular ad hoc Network (VANET) are both trustworthy. The method updates the belief based on the consistency of the data that both entities provide. The evaluation shows that the proposed method is able to identify forged information.
2020-12-02
Scheffer, V., Ipach, H., Becker, C..  2019.  Distribution Grid State Assessment for Control Reserve Provision Using Boundary Load Flow. 2019 IEEE Milan PowerTech. :1—6.

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

2020-11-16
Shen, N., Yeh, J., Chen, C., Chen, Y., Zhang, Y..  2019.  Ensuring Query Completeness in Outsourced Database Using Order-Preserving Encryption. 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom). :776–783.
Nowadays database outsourcing has become business owners' preferred option and they are benefiting from its flexibility, reliability, and low cost. However, because database service providers cannot always be fully trusted and data owners will no longer have a direct control over their own data, how to make the outsourced data secure becomes a hot research topic. From the data integrity protection aspect, the client wants to make sure the data returned is correct, complete, and up-to-date. Previous research work in literature put more efforts on data correctness, while data completeness is still a challenging problem to solve. There are some existing works that tried to protect the completeness of data. Unfortunately, these solutions were considered not fully solving the problem because of their high communication or computation overhead. The implementations and limitations of existing works will be further discussed in this paper. From the data confidentiality protection aspect, order-preserving encryption (OPE) is a widely used encryption scheme in protecting data confidentiality. It allows the client to perform range queries and some other operations such as GROUP BY and ORDER BY over the OPE encrypted data. Therefore, it is worthy to develop a solution that allows user to verify the query completeness for an OPE encrypted database so that both data confidentiality and completeness are both protected. Inspired by this motivation, we propose a new data completeness protecting scheme by inserting fake tuples into databases. Both the real and fake tuples are OPE encrypted and thus the cloud server cannot distinguish among them. While our new scheme is much more efficient than all existing approaches, the level of security protection remains the same.
2020-10-05
Ong, Desmond, Soh, Harold, Zaki, Jamil, Goodman, Noah.  2019.  Applying Probabilistic Programming to Affective Computing. IEEE Transactions on Affective Computing. :1—1.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach

2020-09-14
Lochbihler, Andreas, Sefidgar, S. Reza, Basin, David, Maurer, Ueli.  2019.  Formalizing Constructive Cryptography using CryptHOL. 2019 IEEE 32nd Computer Security Foundations Symposium (CSF). :152–15214.
Computer-aided cryptography increases the rigour of cryptographic proofs by mechanizing their verification. Existing tools focus mainly on game-based proofs, and efforts to formalize composable frameworks such as Universal Composability have met with limited success. In this paper, we formalize an instance of Constructive Cryptography, a generic theory allowing for clean, composable cryptographic security statements. Namely, we extend CryptHOL, a framework for game-based proofs, with an abstract model of Random Systems and provide proof rules for their equality and composition. We formalize security as a special kind of system construction in which a complex system is built from simpler ones. As a simple case study, we formalize the construction of an information-theoretically secure channel from a key, a random function, and an insecure channel.
2020-09-11
Spradling, Matthew, Allison, Mark, Tsogbadrakh, Tsenguun, Strong, Jay.  2019.  Toward Limiting Social Botnet Effectiveness while Detection Is Performed: A Probabilistic Approach. 2019 International Conference on Computational Science and Computational Intelligence (CSCI). :1388—1391.
The prevalence of social botnets has increased public distrust of social media networks. Current methods exist for detecting bot activity on Twitter, Reddit, Facebook, and other social media platforms. Most of these detection methods rely upon observing user behavior for a period of time. Unfortunately, the behavior observation period allows time for a botnet to successfully propagate one or many posts before removal. In this paper, we model the post propagation patterns of normal users and social botnets. We prove that a botnet may exploit deterministic propagation actions to elevate a post even with a small botnet population. We propose a probabilistic model which can limit the impact of social media botnets until they can be detected and removed. While our approach maintains expected results for non-coordinated activity, coordinated botnets will be detected before propagation with high probability.
2020-08-17
Hu, Jianxing, Huo, Dongdong, Wang, Meilin, Wang, Yazhe, Zhang, Yan, Li, Yu.  2019.  A Probability Prediction Based Mutable Control-Flow Attestation Scheme on Embedded Platforms. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :530–537.
Control-flow attacks cause powerful threats to the software integrity. Remote attestation for control flow is a crucial security service for ensuring the software integrity on embedded platforms. The fine-grained remote control-flow attestation with execution-profiling Control-Flow Graph (CFG) is applied to defend against control-flow attacks. It is a safe scheme but it may influence the runtime efficiency. In fact, we find out only the vulnerable parts of a program need being attested at costly fine-grained level to ensure the security, and the remaining normal parts just need a lightweight coarse-grained check to reduce the overhead. We propose Mutable Granularity Control-Flow Attestation (MGC-FA) scheme, which bases on a probabilistic model, to distinguish between the vulnerable and normal parts in the program and combine fine-grained and coarse-grained control-flow attestation schemes. MGC-FA employs the execution-profiling CFG to apply the remote control-flow attestation scheme on embedded devices. MGC-FA is implemented on Raspberry Pi with ARM TrustZone and the experimental results show its effect on balancing the relationship between runtime efficiency and control-flow security.
2020-07-24
Huo, Weiqian, Pei, Jisheng, Zhang, Ke, Ye, Xiaojun.  2014.  KP-ABE with Attribute Extension: Towards Functional Encryption Schemes Integration. 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming. :230—237.

To allow fine-grained access control of sensitive data, researchers have proposed various types of functional encryption schemes, such as identity-based encryption, searchable encryption and attribute-based encryption. We observe that it is difficult to define some complex access policies in certain application scenarios by using these schemes individually. In this paper, we attempt to address this problem by proposing a functional encryption approach named Key-Policy Attribute-Based Encryption with Attribute Extension (KP-ABE-AE). In this approach, we utilize extended attributes to integrate various encryption schemes that support different access policies under a common top-level KP-ABE scheme, thus expanding the scope of access policies that can be defined. Theoretical analysis and experimental studies are conducted to demonstrate the applicability of the proposed KP-ABE-AE. We also present an optimization for a special application of KP-ABE-AE where IPE schemes are integrated with a KP-ABE scheme. The optimization results in an integrated scheme with better efficiency when compared to the existing encryption schemes that support the same scope of access policies.

2020-07-20
Masood, Raziqa, Pandey, Nitin, Rana, Q. P..  2017.  An approach of dredging the interconnected nodes and repudiating attacks in cloud network. 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). :49–53.
In cloud computing environment, there are malignant nodes which create a huge problem to transfer data in communication. As there are so many models to prevent the data over the network, here we try to prevent or make secure to the network by avoiding mallicious nodes in between the communication. So the probabiliostic approach what we use here is a coherent tool to supervise the security challenges in the cloud environment. The matter of security for cloud computing is a superficial quality of service from cloud service providers. Even, cloud computing dealing everyday with new challenges, which is in process to well investigate. This research work draws the light on aspect regarding with the cloud data transmission and security by identifying the malignanat nodes in between the communication. Cloud computing network shared the common pool of resources like hardware, framework, platforms and security mechanisms. therefore Cloud Computing cache the information and deliver the secure transaction of data, so privacy and security has become the bone of contention which hampers the process to execute safely. To ensure the security of data in cloud environment, we proposed a method by implementing white box cryptography on RSA algorithm and then we work on the network, and find the malignant nodes which hampering the communication by hitting each other in the network. Several existing security models already have been deployed with security attacks. A probabilistic authentication and authorization approach is introduced to overcome this attack easily. It observes corrupted nodes before hitting with maximum probability. here we use a command table to conquer the malignant nodes. then we do the comparative study and it shows the probabilistic authentication and authorization protocol gives the performance much better than the old ones.
2020-07-13
Grüner, Andreas, Mühle, Alexander, Meinel, Christoph.  2019.  Using Probabilistic Attribute Aggregation for Increasing Trust in Attribute Assurance. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). :633–640.
Identity management is an essential cornerstone of securing online services. Service provisioning relies on correct and valid attributes of a digital identity. Therefore, the identity provider is a trusted third party with a specific trust requirement towards a verified attribute supply. This trust demand implies a significant dependency on users and service providers. We propose a novel attribute aggregation method to reduce the reliance on one identity provider. Trust in an attribute is modelled as a combined assurance of several identity providers based on probability distributions. We formally describe the proposed aggregation model. The resulting trust model is implemented in a gateway that is used for authentication with self-sovereign identity solutions. Thereby, we devise a service provider specific web of trust that constitutes an intermediate approach bridging a global hierarchical model and a locally decentralized peer to peer scheme.
2020-07-10
Podlesny, Nikolai J., Kayem, Anne V.D.M., Meinel, Christoph.  2019.  Identifying Data Exposure Across Distributed High-Dimensional Health Data Silos through Bayesian Networks Optimised by Multigrid and Manifold. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :556—563.

We present a novel, and use case agnostic method of identifying and circumventing private data exposure across distributed and high-dimensional data repositories. Examples of distributed high-dimensional data repositories include medical research and treatment data, where oftentimes more than 300 describing attributes appear. As such, providing strong guarantees of data anonymity in these repositories is a hard constraint in adhering to privacy legislation. Yet, when applied to distributed high-dimensional data, existing anonymisation algorithms incur high levels of information loss and do not guarantee privacy defeating the purpose of anonymisation. In this paper, we address this issue by using Bayesian networks to handle data transformation for anonymisation. By evaluating every attribute combination to determine the privacy exposure risk, the conditional probability linking attribute pairs is computed. Pairs with a high conditional probability expose the risk of deanonymisation similar to quasi-identifiers and can be separated instead of deleted, as in previous algorithms. Attribute separation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in information loss. In other words, assimilating the conditional probability of outliers directly in the adjacency matrix in a greedy fashion is quick and thwarts de-anonymisation. Since identifying every privacy violating attribute combination is a W[2]-complete problem, we optimise the procedure with a multigrid solver method by evaluating the conditional probabilities between attribute pairs, and aggregating state space explosion of attribute pairs through manifold learning. Finally, incremental processing of new data is achieved through inexpensive, continuous (delta) learning.

2020-04-17
Alim, Adil, Zhao, Xujiang, Cho, Jin-Hee, Chen, Feng.  2019.  Uncertainty-Aware Opinion Inference Under Adversarial Attacks. 2019 IEEE International Conference on Big Data (Big Data). :6—15.

Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.

2020-03-09
Tun, Hein, Lupin, Sergey, Than, Ba Hla, Nay Zaw Linn, Kyaw, Khaing, Min Thu.  2019.  Estimation of Information System Security Using Hybrid Simulation in AnyLogic. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1829–1834.
Nowadays the role of Information systems in our life has greatly increased, which has become one of the biggest challenges for citizens, organizations and governments. Every single day we are becoming more and more dependent on information and communication technology (ICT). A major goal of information security is to find the best ways to mitigate the risks. The context-role and perimeter protection approaches can reduce and prevent an unauthorized penetration to protected zones and information systems inside the zones. The result of this work can be useful for the security system analysis and optimization of their organizations.