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2021-07-27
Biswal, Milan, Misra, Satyajayant, Tayeen, Abu S..  2020.  Black Box Attack on Machine Learning Assisted Wide Area Monitoring and Protection Systems. 2020 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT). :1–5.
The applications for wide area monitoring, protection, and control systems (WAMPC) at the control center, help with providing resilient, efficient, and secure operation of the transmission system of the smart grid. The increased proliferation of phasor measurement units (PMUs) in this space has inspired many prudent applications to assist in the process of decision making in the control centers. Machine learning (ML) based decision support systems have become viable with the availability of abundant high-resolution wide area operational PMU data. We propose a deep neural network (DNN) based supervisory protection and event diagnosis system and demonstrate that it works with very high degree of confidence. The system introduces a supervisory layer that processes the data streams collected from PMUs and detects disturbances in the power systems that may have gone unnoticed by the local monitoring and protection system. Then, we investigate compromise of the insights of this ML based supervisory control by crafting adversaries that corrupt the PMU data via minimal coordinated manipulation and identification of the spatio-temporal regions in the multidimensional PMU data in a way that the DNN classifier makes wrong event predictions.
Reviriego, Pedro, Rottenstreich, Ori.  2020.  Pollution Attacks on Counting Bloom Filters for Black Box Adversaries. 2020 16th International Conference on Network and Service Management (CNSM). :1–7.
The wide adoption of Bloom filters makes their security an important issue to be addressed. For example, an attacker can increase their error rate through polluting and eventually saturating the filter by inserting elements that set to one a large number of positions in the filter. This is known as a pollution attack and requires that the attacker knows the hash functions used to construct the filter. Such information is not available in many practical settings and in addition a simple protection can be achieved through using a random salt in the hash functions. The same pollution attacks can also be done to counting Bloom filters that in addition to insertions and lookups support removals. This paper considers pollution attacks on counting Bloom filters. We describe two novel pollution attacks that do not require any knowledge of the counting Bloom filter implementation details and evaluate them. These methods show that a counting Bloom filter is vulnerable to pollution attacks even when the attacker has only access to the filter as a black box to perform insertions, removals, and lookups.
Bao, Zhida, Zhao, Haojun.  2020.  Evaluation of Adversarial Attacks Based on DL in Communication Networks. 2020 7th International Conference on Dependable Systems and Their Applications (DSA). :251–252.
Deep Neural Networks (DNN) have strong capabilities of memories, feature identifications and automatic analyses, solving various complex problems. However, DNN classifiers have obvious fragility that adding several unnoticeable perturbations to the original examples will lead to the errors in the classifier identification. In the field of communications, the adversarial examples will greatly reduce the accuracy of the signal identification, causing great information security risks. Considering the adversarial examples pose a serious threat to the security of the DNN models, studying their generation mechanisms and testing their attack effects are critical to ensuring the information security of the communication networks. This paper will study the generation of the adversarial examples and the influences of the adversarial examples on the accuracy of the DNN-based communication signal identification. Meanwhile, this paper will study the influences of the adversarial examples under the white-box models and black-box models, and explore the adversarial attack influences of the factors such as perturbation levels and iterative steps. The insights of this study would be helpful for ensuring the security of information networks and designing robust DNN communication networks.
Xiao, Wenli, Jiang, Hao, Xia, Song.  2020.  A New Black Box Attack Generating Adversarial Examples Based on Reinforcement Learning. 2020 Information Communication Technologies Conference (ICTC). :141–146.
Machine learning can be misled by adversarial examples, which is formed by making small changes to the original data. Nowadays, there are kinds of methods to produce adversarial examples. However, they can not apply non-differentiable models, reduce the amount of calculations, and shorten the sample generation time at the same time. In this paper, we propose a new black box attack generating adversarial examples based on reinforcement learning. By using deep Q-learning network, we can train the substitute model and generate adversarial examples at the same time. Experimental results show that this method only needs 7.7ms to produce an adversarial example, which solves the problems of low efficiency, large amount of calculation and inapplicable to non-differentiable model.
Xu, Jiahui, Wang, Chen, Li, Tingting, Xiang, Fengtao.  2020.  Improved Adversarial Attack against Black-box Machine Learning Models. 2020 Chinese Automation Congress (CAC). :5907–5912.
The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L2 distance. Second, we innovatively combine a swarm intelligence algorithm - Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.
Fan, Wenshu, Li, Hongwei, Jiang, Wenbo, Xu, Guowen, Lu, Rongxing.  2020.  A Practical Black-Box Attack Against Autonomous Speech Recognition Model. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1–6.
With the wild applications of machine learning (ML) technology, automatic speech recognition (ASR) has made great progress in recent years. Despite its great potential, there are various evasion attacks of ML-based ASR, which could affect the security of applications built upon ASR. Up to now, most studies focus on white-box attacks in ASR, and there is almost no attention paid to black-box attacks where attackers can only query the target model to get output labels rather than probability vectors in audio domain. In this paper, we propose an evasion attack against ASR in the above-mentioned situation, which is more feasible in realistic scenarios. Specifically, we first train a substitute model by using data augmentation, which ensures that we have enough samples to train with a small number of times to query the target model. Then, based on the substitute model, we apply Differential Evolution (DE) algorithm to craft adversarial examples and implement black-box attack against ASR models from the Speech Commands dataset. Extensive experiments are conducted, and the results illustrate that our approach achieves untargeted attacks with over 70% success rate while still maintaining the authenticity of the original data well.
2021-07-08
Chiariotti, Federico, Signori, Alberto, Campagnaro, Filippo, Zorzi, Michele.  2020.  Underwater Jamming Attacks as Incomplete Information Games. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1033—1038.
Autonomous Underwater Vehicles (AUVs) have several fundamental civilian and military applications, and Denial of Service (DoS) attacks against their communications are a serious threat. In this work, we analyze such an attack using game theory in an asymmetric scenario, in which the node under attack does not know the position of the jammer that blocks its signals. The jammer has a dual objective, namely, disrupting communications and forcing the legitimate transmitter to spend more energy protecting its own transmissions. Our model shows that, if both nodes act rationally, the transmitter is able to quickly reduce its disadvantage, estimating the location of the jammer and responding optimally to the attack.
Su, Yishan, Zhang, Ting, Jin, Zhigang, Guo, Lei.  2020.  An Anti-Attack Trust Mechanism Based on Collaborative Spectrum Sensing for Underwater Acoustic Sensor Networks. Global Oceans 2020: Singapore – U.S. Gulf Coast. :1—5.
The main method for long-distance underwater communication is underwater acoustic communication(UAC). The bandwidth of UAC channel is narrow and the frequency band resources are scarce. Therefore, it is important to improve the frequency band utilization of UAC system. Cognitive underwater acoustic (CUA) technology is an important method. CUA network can share spectrum resources with the primary network. Spectrum sensing (SS) technology is the premise of realizing CUA. Therefore, improving the accuracy of spectral sensing is the main purpose of this paper. However, the realization of underwater SS technology still faces many difficulties. First, underwater energy supplies are scarce, making it difficult to apply complex algorithms. Second, and more seriously, CUA network can sometimes be attacked and exploited by hostile forces, which will not only lead to data leakage, but also greatly affect the accuracy of SS. In order to improve the utilization of underwater spectrum and avoid attack, an underwater spectrum sensing model based on the two-threshold energy detection method and K of M fusion decision method is established. Then, the trust mechanism based on beta function and XOR operation are proposed to combat individual attack and multi-user joint attack (MUJA) respectively. Finally, simulation result shows the effectiveness of these methods.
Gomathi, R. M., Keerthana, Kare, Priyanka, Kamatham, Anandhi, T..  2020.  Energy Efficient Data Gathering Scheme in Underwater Sensor Networks. 2020 5th International Conference on Communication and Electronics Systems (ICCES). :480—485.
In this paper, an energy routing algorithm, called SAODV (secure Ad hoc On Demand Distance Vector) is designed for ad hoc mobile networks. SAODV is capable of both unicast and multicast routing. It is an on demand algorithm, meaning that it builds routes between nodes only as desired by source nodes. It maintains these routes as long as they are needed by the sources. Additionally, SAODV forms trees which connect multicast group members. The trees are composed of the group members and the nodes needed to connect the members. Extensive simulations are conducted to study the power consumption, the end-to-end delay, and the network throughput of our protocols compared with existing protocols. Efficiently handling losses in wireless environments, therefore, has significant importance. Even under benign conditions, various factors, like fading, interference, multi-path effects, and collisions, lead to heavy loss rates on wireless links.
Signori, Alberto, Campagnaro, Filippo, Wachlin, Kim-Fabian, Nissen, Ivor, Zorzi, Michele.  2020.  On the Use of Conversation Detection to Improve the Security of Underwater Acoustic Networks. Global Oceans 2020: Singapore – U.S. Gulf Coast. :1—8.
Security is one of the key aspects of underwater acoustic networks, due to the critical importance of the scenarios in which these networks can be employed. For example, attacks performed to military underwater networks or to assets deployed for tsunami prevention can lead to disastrous consequences. Nevertheless, countermeasures to possible network attacks have not been widely investigated so far. One way to identify possible attackers is by using reputation, where a node gains trust each time it exhibits a good behavior, and loses trust each time it behaves in a suspicious way. The first step for analyzing if a node is behaving in a good way is to inspect the network traffic, by detecting all conversations. This paper proposes both centralized and decentralized algorithms for performing this operation, either from the network or from the node perspective. While the former can be applied only in post processing, the latter can also be used in real time by each node, and so can be used for creating the trust value. To evaluate the algorithms, we used real experimental data acquired during the EDA RACUN project (Robust Underwater Communication in Underwater Networks).
Li, Sichun, Jin, Xin, Yao, Sibing, Yang, Shuyu.  2020.  Underwater Small Target Recognition Based on Convolutional Neural Network. Global Oceans 2020: Singapore – U.S. Gulf Coast. :1—7.
With the increasingly extensive use of diver and unmanned underwater vehicle in military, it has posed a serious threat to the security of the national coastal area. In order to prevent the underwater diver's impact on the safety of water area, it is of great significance to identify underwater small targets in time to make early warning for it. In this paper, convolutional neural network is applied to underwater small target recognition. The recognition targets are diver, whale and dolphin. Due to the time-frequency spectrum can reflect the essential features of underwater target, convolutional neural network can learn a variety of features of the acoustic signal through the image processed by the time-frequency spectrum, time-frequency image is input to convolutional neural network to recognize the underwater small targets. According to the study of learning rate and pooling mode, the network parameters and structure suitable for underwater small target recognition in this paper are selected. The results of data processing show that the method can identify underwater small targets accurately.
Ozmen, Alper, Yildiz, Huseyin Ugur, Tavli, Bulent.  2020.  Impact of Minimizing the Eavesdropping Risks on Lifetime of Underwater Acoustic Sensor Networks. 2020 28th Telecommunications Forum (℡FOR). :1—4.
Underwater Acoustic Sensor Networks (UASNs) are often deployed in hostile environments, and they face many security threats. Moreover, due to the harsh characteristics of the underwater environment, UASNs are vulnerable to malicious attacks. One of the most dangerous security threats is the eavesdropping attack, where an adversary silently collects the information exchanged between the sensor nodes. Although careful assignment of transmission power levels and optimization of data flow paths help alleviate the extent of eavesdropping attacks, the network lifetime can be negatively affected since routing could be established using sub-optimal paths in terms of energy efficiency. In this work, two optimization models are proposed where the first model minimizes the potential eavesdropping risks in the network while the second model maximizes the network lifetime under a certain level of an eavesdropping risk. The results show that network lifetimes obtained when the eavesdropping risks are minimized significantly shorter than the network lifetimes obtained without considering any eavesdropping risks. Furthermore, as the countermeasures against the eavesdropping risks are relaxed, UASN lifetime is shown to be prolonged, significantly.
Chandavarkar, B. R., Gadagkar, Akhilraj V..  2020.  Mitigating Localization and Neighbour Spoofing Attacks in Underwater Sensor Networks. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—5.
The location information of a node is one of the essential attributes used in most underwater communication routing algorithms to identify a candidate forwarding node by any of the sources. The exact location information of a node exchanged with its neighbours' in plain text and the absence of node authentication results in some of the attacks such as Sybil attack, Blackhole attack, and Wormhole attack. Moreover, the severe consequence of these attacks is Denial of Service (DoS), poor network performance, reduced network lifetime, etc. This paper proposes an anti-Spoof (a-Spoof) algorithm for mitigating localization and neighbour spoofing attacks in UASN. a-Spoof uses three pre-shared symmetric keys to share the location. Additionally, location integrity provided through the hash function. Further, the performance of a-Spoof demonstrated through its implementation in UnetStack with reference to end-to-end packet delay and the number of hops.
Khalid, Muhammad, Zhao, Ruiqin, Wang, Xin.  2020.  Node Authentication in Underwater Acoustic Sensor Networks Using Time-Reversal. Global Oceans 2020: Singapore – U.S. Gulf Coast. :1—4.
Physical layer authentication scheme for node authentication using the time-reversal (TR) process and the location-specific key feature of the channel impulse response (CIR) in an underwater time-varying multipath environment is proposed. TR is a well-known signal focusing technique in signal processing; this focusing effect is used by the database maintaining node to authenticate the sensor node by convolving the estimated CIR from a probe signal with its database of CIRs. Maximum time-reversal resonating strength (MTRRS) is calculated to make an authentication decision. This work considers a static underwater acoustic sensor network (UASN) under the “Alice- Bob-Eve” scenario. The performance of the proposed scheme is expressed by the Probability of Detection (PD) and the Probability of False Alarm (PFA).
Chaturvedi, Amit Kumar, Chahar, Meetendra Singh, Sharma, Kalpana.  2020.  Proposing Innovative Perturbation Algorithm for Securing Portable Data on Cloud Servers. 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART). :360—364.
Cloud computing provides an open architecture and resource sharing computing platform with pay-per-use model. It is now a popular computing platform and most of the new internet based computing services are on this innovation supported environment. We consider it as innovation supported because developers are more focused here on the service design, rather on arranging the infrastructure, network, management of the resources, etc. These all things are available in cloud computing on hired basis. Now, a big question arises here is the security of data or privacy of data because the service provider is already using the infrastructure, network, storage, processors, and other more resources from the third party. So, the security or privacy of the portable user's data is the main motivation for writing this research paper. In this paper, we are proposing an innovative perturbation algorithm MAP() to secure the portable user's data on the cloud server.
Rao, Liting, Xie, Qingqing, Zhao, Hui.  2020.  Data Sharing for Multiple Groups with Privacy Preservation in the Cloud. 2020 International Conference on Internet of Things and Intelligent Applications (ITIA). :1—5.
With almost unlimited storage capacity and low maintenance cost, cloud storage becomes a convenient and efficient way for data sharing among cloud users. However, this introduces the challenges of access control and privacy protection when data sharing for multiple groups, as each group usually has its own encryption and access control mechanism to protect data confidentiality. In this paper, we propose a multiple-group data sharing scheme with privacy preservation in the cloud. This scheme constructs a flexible access control framework by using group signature, ciphertext-policy attribute-based encryption and broadcast encryption, which supports both intra-group and cross-group data sharing with anonymous access. Furthermore, our scheme supports efficient user revocation. The security and efficiency of the scheme are proved thorough analysis and experiments.
Ilokah, Munachiso, Eklund, J. Mikael.  2020.  A Secure Privacy Preserving Cloud-based Framework for Sharing Electronic Health Data*. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). :5592—5597.
There exists a need for sharing user health data, especially with institutes for research purposes, in a secure fashion. This is especially true in the case of a system that includes a third party storage service, such as cloud computing, which limits the control of the data owner. The use of encryption for secure data storage continues to evolve to meet the need for flexible and fine-grained access control. This evolution has led to the development of Attribute Based Encryption (ABE). The use of ABE to ensure the security and privacy of health data has been explored. This paper presents an ABE based framework which allows for the secure outsourcing of the more computationally intensive processes for data decryption to the cloud servers. This reduces the time needed for decryption to occur at the user end and reduces the amount of computational power needed by users to access data.
Abdo, Mahmoud A., Abdel-Hamid, Ayman A., Elzouka, Hesham A..  2020.  A Cloud-based Mobile Healthcare Monitoring Framework with Location Privacy Preservation. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). :1—8.
Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user's location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user's location will affect the user's privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user's vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user's data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user's health state, and crowdsensing for collecting information about a person's privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.
Raja, S. Kanaga Suba, Sathya, A., Priya, L..  2020.  A Hybrid Data Access Control Using AES and RSA for Ensuring Privacy in Electronic Healthcare Records. 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). :1—5.
In the current scenario, the data owners would like to access data from anywhere and anytime. Hence, they will store their data in public or private cloud along with encryption and particular set of attributes to access control on the cloud data. While uploading the data into public or private cloud they will assign some attribute set to their data. If any authorized cloud user wants to download their data they should enter that particular attribute set to perform further actions on the data owner's data. A cloud user wants to register their details under cloud organization to access the data owner's data. Users wants to submit their details as attributes along with their designation. Based on the Users details Semi-Trusted Authority generates decryption keys to get control on owner's data. A user can perform a lot of operation over the cloud data. If the user wants to read the cloud data he needs to be entering some read related, and if he wants to write the data he needs to be entering write related attribute. For each and every action user in an organization would be verified with their unique attribute set. These attributes will be stored by the admins to the authorized users in cloud organization. These attributes will be stored in the policy files in a cloud. Along with this attribute,a rule based engine is used, to provide the access control to user. If any user leaks their decryption key to the any malicious user data owners wants to trace by sending audit request to auditor and auditor will process the data owners request and concludes that who is the convict.
Cesconetto, Jonas, Silva, Luís A., Valderi Leithardt, R. Q., Cáceres, María N., Silva, Luís A., Garcia, Nuno M..  2020.  PRIPRO:Solution for user profile control and management based on data privacy. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). :1—6.
Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles).
Nooh, Sameer A..  2020.  Cloud Cryptography: User End Encryption. 2020 International Conference on Computing and Information Technology (ICCIT-1441). :1—4.
Cloud computing has made the life of individual users and work of business corporations so much easier by providing them data storage services at very low costs. Individual users can store and access their data through shared cloud storage service anywhere anytime. Similarly, business corporation consumers of cloud computing can store, manage, process and access their big data with quite an ease. However, the security and privacy of users' data remains vulnerable in cloud computing Availability, integrity and confidentiality are the three primary elements that users consider before signing up for cloud computing services. Many public and private cloud services have experienced security breaches and unauthorized access incidents. This paper suggests user end cryptography of data before uploading it to a cloud storage service platform like Google Drive, Microsoft, Amazon and CloudSim etc. The proposed cryptography algorithm is based on symmetric key cryptography model and has been implemented on Amazon S3 cloud space service.
Kanchanadevi, P., Raja, Laxmi, Selvapandian, D., Dhanapal, R..  2020.  An Attribute Based Encryption Scheme with Dynamic Attributes Supporting in the Hybrid Cloud. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :271—273.
Cloud computing is the flexible platform to outsource the data from local server to commercial cloud. However cloud provides tremendous benefits to user, data privacy and data leakage reduce the attention of cloud. For protecting data privacy and reduce data leakage various techniques has to be implemented in cloud. There are various types of cloud environment, but we concentrate on Hybrid cloud. Hybrid cloud is nothing but combination of more than two or more cloud. Where critical operations are performed in private cloud and non critical operations are performed in public cloud. So, it has numerous advantages and criticality too. In this paper, we focus on data security through encryption scheme over Hybrid Cloud. There are various encryption schemes are close to us but it also have data security issues. To overcome these issues, Attribute Based Encryption Scheme with Dynamic Attributes Supporting (ABE-DAS) has proposed. Attribute based Encryption Scheme with Dynamic Attributes Supporting technique enhance the security of the data in hybrid cloud.
Li, Yan.  2020.  User Privacy Protection Technology of Tennis Match Live Broadcast from Media Cloud Platform Based on AES Encryption Algorithm. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). :267—269.
With the improvement of the current Internet software and hardware performance, cloud storage has become one of the most widely used applications. This paper proposes a user privacy protection algorithm suitable for tennis match live broadcast from media cloud platform. Through theoretical and experimental verification, this algorithm can better protect the privacy of users in the live cloud platform. This algorithm is a ciphertext calculation algorithm based on data blocking. Firstly, plaintext data are grouped, then AES ciphertext calculation is performed on each group of plaintext data simultaneously and respectively, and finally ciphertext data after grouping encryption is spliced to obtain final ciphertext data. Experimental results show that the algorithm has the characteristics of large key space, high execution efficiency, ciphertext statistics and good key sensitivity.
Kunz, Immanuel, Schneider, Angelika, Banse, Christian.  2020.  Privacy Smells: Detecting Privacy Problems in Cloud Architectures. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1324—1331.
Many organizations are still reluctant to move sensitive data to the cloud. Moreover, data protection regulations have established considerable punishments for violations of privacy and security requirements. Privacy, however, is a concept that is difficult to measure and to demonstrate. While many privacy design strategies, tactics and patterns have been proposed for privacy-preserving system design, it is difficult to evaluate an existing system with regards to whether these strategies have or have not appropriately been implemented. In this paper we propose indicators for a system's non-compliance with privacy design strategies, called privacy smells. To that end we first identify concrete metrics that measure certain aspects of existing privacy design strategies. We then define smells based on these metrics and discuss their limitations and usefulness. We identify these indicators on two levels of a cloud system: the data flow level and the access control level. Using a cloud system built in Microsoft Azure we show how the metrics can be measured technically and discuss the differences to other cloud providers, namely Amazon Web Services and Google Cloud Platform. We argue that while it is difficult to evaluate the privacy-awareness in a cloud system overall, certain privacy aspects in cloud systems can be mapped to useful metrics that can indicate underlying privacy problems. With this approach we aim at enabling cloud users and auditors to detect deep-rooted privacy problems in cloud systems.
2021-07-07
Mengli, Zhou, Fucai, Chen, Wenyan, Liu, Hao, Liang.  2020.  Negative Feedback Dynamic Scheduling Algorithm based on Mimic Defense in Cloud Environment. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :2265–2270.
The virtualization technology in cloud environment brings some data and privacy security issues to users. Aiming at the problems of virtual machines singleness, homogeneity and static state in cloud environment, a negative feedback dynamic scheduling algorithm is proposed. This algorithm is based on mimic defense and creates multiple virtual machines to complete user request services together through negative feedback control mechanism which can achieve real-time monitor of the running state of virtual machines. When virtual machines state is found to be inconsistent, this algorithm will dynamically change its execution environment, resulting in the attacker's information collection and vulnerability exploitation process being disrupting. Experiments show that the algorithm can better solve security threats caused by the singleness, homogeneity and static state of virtual machines in the cloud, and improve security and reliability of cloud users.