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The development of the Internet of things (IoT) has given birth to new applications and services. Accordingly, because the IoT application system collects a large amount of data containing sensitive information, there are also new challenges in data security and privacy preservation. To locate the medical Internet of things (mIoT), this paper proposes a multi-keyword searchable encryption scheme based on attributes supporting the data privacy preservation and integrity verification of the electronic health files stored on the IoT cloud platform on the basis of ensuring the application business functions of the mIoT. (1) To ensure data confidentiality and fine-grained search authorization, the attribute-based encryption mechanism is introduced to encrypt the symmetric key. Moreover, anonymous key generation tampers semi-Trusted Centre Authority's (CA) decryption of all ciphertexts of users and access policy is hidden to prevent access policy from leaking. (2) Convergence key is adopted to encrypt electronic health records (EHRs), thereby providing double verification of the correctness of the search results and partial decryption results. (3) A time division mechanism is also established to provide the access validity of shared EHRs. (4) Fixed length ciphertext and outsourcing decryption mechanisms are used to reduce the calculation on the user side, in which the majority of the decryption calculations are done by the cloud server. Formal proofs and performance evaluation show that the proposed scheme can achieve data security, keyword security, integrity verification, hidden policy and collusion resistance, and has high computational efficiency and search efficiency.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as well as dynamic policy-updating. In this paper, we design a security-enhanced Attribute Cuckoo Filter (se-ACF) to hide the access policy and propose a new CP-ABE system, called Privacy-Preserving Policy Updating ABE (3PU-ABE), which effectively integrates policy hiding and policy updating. We conduct rigorous security analysis and performance evaluation of 3PU-ABE. The results indicate that 3PU-ABE completely hides the access policy without affecting the decryption, and entails better policy-updating efficiency than similar works.

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this paper, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

The public cloud data integrity auditing technique is used to check the integrity of cloud data through the Third Party Auditor. In order to make it more practical, we propose a new paradigm called integrity auditing based on the keyword with sensitive information privacy for encrypted cloud data. This paradigm is designed for one of the most common scenario, that is, the user concerns the integrity of a portion of encrypted cloud files that contain his/her interested keywords. In our proposed scheme, the TPA who is only provided with the encrypted keyword, can audit the integrity of all encrypted cloud files that contain the user's interested keyword. Meanwhile, the TPA cannot deduce the sensitive information about which files contain the keyword and how many files contain this keyword. These salient features are realized by leveraging a newly proposed Relation Authentication Label (RAL). The RAL can not only authenticate the relation that files contain the queried keyword, but also be used to generate the auditing proof without sensitive information exposure. We give concrete security analysis showing that the proposed scheme satisfies correctness, auditing soundness and sensitive information privacy. We also conduct the detailed experiments to show the efficiency of our scheme.

The proliferation of smartphones equipped with various sensors created a large user base, which includes robots (smartphone controlled) as well. The high processor enabled smartphones are used as software robots to track hour-to-hour activities of users using on-board motion sensors. The magnetometer, accelerometer, and gyroscope are non-permission based motion sensors. The logs of these sensors create discriminative patterns to identify users’ movements. This leverages an adversary to cause privacy threats using these non-permission based sensors without user consent. Recent research works also highlighted the privacy threats due to these motion sensors in smartphones. Therefore, in this paper, we present a potential misuse of technology where smartphones can be hacked by PIN Inference attack endangering the physical and software robots’ controlled user activities. We perform experiments to collect motion sensors’ data and infer 4-digit PIN of a smartphone. Our strategy is to detect every key pressed one by one constituting a 4-digit PIN. Our inference results are promising and prove that an adversary can make use of sensors’ data to infiltrate user’s sensitive information from smartphones. Our proposed model infers 84% of the PINs correctly within 40 attempts when tested on 50 PINs. Given real Android devices with different users, we can identify the PIN of smartphones by training our machine learning based model. We also compare our approach with existing state-of-the-art approaches to show the efficacy of our attack model.

In this study, we elaborate on a design and realization of fuzzy rule-based model in the horizontal federated learning framework. Traditional machine learning in distributed environment often involves sharing sensitive information with other sites or transferring data to a central server on which a global model is trained. These situations increase the communication overhead and pose serious threats to the privacy of sensitive data. Federated learning opens up the possibility for collaboratively training a global model on a basis of distributed on-site data without sacrificing data privacy. While fuzzy rule-based models have been used in system modeling due to their strong modeling abilities and good interpretability, the implementation of fuzzy rule-based models in a distributed environment without compromising data privacy still requires careful consideration. This work proposes a two-step federated learning approach to train a global model on a basis of private data located across different sites without their centralization. The first step concerns the determination of the structure of the data through federated collaborative clustering. Subsequently, a shared global model is trained jointly by all the participating clients. An advantage of the propose method is that it achieves high accuracy without violating data privacy. A series of experimental studies are conducted to gain a detailed insight into the realization steps and demonstrate the effectiveness of the proposed method.

Benefiting from the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment.

The advance of smart eHealthcare has greatly enabled patients to enjoy high-quality healthcare services, e.g., disease prediction. Meanwhile, to support the dramatic increase of healthcare data, healthcare centers often outsource the on-premises data to the cloud. However, since the healthcare data contain some sensitive information and the cloud server is not fully trusted, healthcare centers need to encrypt the data before outsourcing them. Unfortunately, data encryption inevitably hinders some advanced data applications like the similarity range query. Although many studies on similarity range queries over encrypted data have been reported, most of them still have some limitations in security, efficiency, accuracy, and practicality. Aiming at this challenge, in this paper, we propose an efficient, privacy-preserving, and practical similarity range query (EPSim) scheme. Specifically, we first present a modified asymmetric scalar-product-preserving encryption (ASPE) scheme and prove its security. Then, we introduce a Quadsector tree to represent the data and employ a filtration condition to design an algorithm for efficient similarity range queries over the Quadsector tree. Finally, we propose our EPSim scheme by integrating the modified ASPE scheme and Quadsector tree. Detailed security analysis and performance evaluations indicate our scheme is really secure and efficient in similarity range queries.

Browser extensions are widely used nowadays to customize users' browsers with more functionalities, meanwhile introduce potential risks due to escalated privileges. Existing security mechanisms, such as Same Origin Policy and Content Security Policy, do not apply to browser extensions that can read and write on web applications at any time. In spite of the state-of-art industrial efforts that rely on centralized management to inspect and detect malicious behaviors massively, the detection-based method cannot analyze fast-evolving behaviors of malicious browser extensions. To this end, we adopt a novel approach to protect users from malicious browser extensions, where we consider the problem of malicious extensions on the side of web applications. From a high level point of view, web developers are allowed to specify sensitive parts in a web application by using our provided software enclave. With our proposed WebEnclave extension installed, when users visit a web application, sensitive information required for the web application to work normally is sealed into an isolated world locally that malicious extensions cannot access. Extensive evaluation of our built prototype shows it can effectively protect user secrets from malicious extensions with negligible performance overhead and usability inconvenience. We also publish source codes for public use.

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a Blockchain based Secure Data Aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers (i.e., Mobile Data Collectors (MDC)) to search and accept tasks, the block header is intergraded with a security label including task security level and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the Improved double bootstrapped Deep Deterministic Policy Gradient (IDDPG), is developed to design energy efficient MDC routes under the constrains that the security levels of MDCs should be higher than the security levels of data aggregation tasks. Simulation results indicate that (i) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency; (ii) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that are used to encrypt the entire EMRs can hide the sensitive information, but results in the shared EMRs unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis even endanger patients' life. In order to solve the above problems, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted while other contents in EMR can be shared among users in this scheme. The doctors and researchers can quickly perform search operations based on keyword to extract the EMRs they requires. But the sensitive information is hidden for the researchers. Furthermore, the correctness of EMRs can be verified when they are extracted from the cloud.

The purposes are to investigate how blockchain can solve the security problems in Intelligent Autonomous Transport System (IATS) and intelligentize the logistics transportation development. Regarding the scarcity of trust and concentration of rights caused by the centralized structure of traditional logistics information systems, a blockchain-based IATS is proposed. The system employs Ethereum as the underlying blockchain to record sensitive information, such as system orders, cargos, and personnel information on the blockchain, ensuring the non-tampering and credibility of data. Simultaneously, an order management module, a warehouse management module, a transportation management module, a transaction management module, and a system management module are established. In the meantime, the Light Gradient Boosting Machine (LightGBM) algorithm is utilized to recommend vehicle and cargo matching during transportation. Finally, the constructed algorithm model is simulated to analyze its performance. Results demonstrate that the security prediction accuracy of the proposed algorithm reaches 88.72%; moreover, the security prediction precision, recall, and F1 of the proposed algorithm are considerably better than those of other algorithms. Furthermore, the actual effect of each algorithm is analyzed. The LightGBM algorithm outperforms other algorithms and unused algorithms in click rate, conversion rate, turnover rate, and average response time. Therefore, the constructed blockchain-based IATS has excellent security performance and prediction accuracy, which provides an experimental basis for the later intelligent logistics transportation development.

With the popularity of social networks, a great variety of new social applications have been generated for impromptu group formation and communications. Among those applications, the subgraph matching has become a hot research area in social networks. Due to the huge cost of managing and computing graph data, it may have to outsource the computations to the cloud server. However, the most critical problem is that the cloud server leaks the graph information during the processing of the graph data, and the external attackers modify the graph information during the transmission on the public channel. Thus, confidentiality and authentication have been critical attributes in the subgraph matching query service. In this paper, we present an efficient and privacy-preserving subgraph matching scheme with authentication in social networks. Using the proposed scheme, the cloud can accomplish the subgraph matching query process without obtaining any sensitive information about the users. Additionally, we achieve data integrity verification and user authentication. Each receiver can verify if the received messages come from the legal sender and have not been tampered. The detailed security and efficiency analysis show that the proposed scheme not only satisfies security requirements but also achieves high-efficiency in local users, and it is suitable for many practical applications.

Wireless body area network (WBAN) and big data techniques indubitably enable the online medical diagnosis system to be more practical. In the system, to make a more accurate diagnosis, doctors wish to obtain some archived medical data records, which are similar to the sensed patient data, to learn from the prior diagnoses. As a practically useful similarity search, the dynamic skyline query can provide doctors with similar data records having all possible relative weights of attributes. Driven by the powerful cloud, the data owner often outsources encrypted data and the corresponding services, e.g., dynamic skyline query services here, to a third-party cloud. As a result, it is required to perform the dynamic skyline query over encrypted data. However, existing schemes are either insecure or inefficient. To address the issue, in this paper, we propose an efficient and privacy-preserving dynamic skyline query scheme and use it in an online medical diagnosis system. Specifically, based on symmetric homomorphic encryption (SHE), we present a set of efficient and secure protocols to achieve various operations, such as less than comparison, equality test, and dominance determination, without leaking any sensitive information to the cloud. With these secure protocols, we carefully design our dynamic skyline query scheme to attain full security and high efficiency at the same time. Detailed security analysis shows that our proposed scheme is indeed privacy-preserving. With extensive experimental evaluations, we show that our proposed scheme outperforms the alternative scheme by two orders of magnitude in the computational cost and at least 8.1× in the communication cost.

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones may exhibit their applications preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.

Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.

This paper considers the privacy-preserving distributed Nash equilibrium seeking strategy design for average aggregative games, in which the players' objective functions are considered to be sensitive information to be protected. In particular, we consider that the game is free of central node and the aggregate information is not directly available to the players. As there is no central authority to provide the aggregate information required by each player to update their actions, a dynamic average consensus protocol is employed to estimate it. To protect the players' privacy, we perturb the transmitted information among the players by independent random noises drawn from Laplace distributions. By synthesizing the perturbed average consensus protocol with a gradient algorithm, a distributed privacy-preserving Nash equilibrium seeking strategy is established for the aggregative games under both fixed and time-varying communication topologies. With explicit quantifications of the mean square errors, the convergence results of the proposed methods are presented. Moreover, it is analytically proven that the proposed algorithm is μ-differentially private. The presented results indicate that there is a tradeoff between the convergence accuracy and the privacy level. Lastly, a numerical example is provided for the verification of the proposed methods.

Unmanned Aerial Vehicles (UAVs) are being used in different emerging domains for accomplishing many critical tasks. However, due to the various constraints like – battery life, computational resources, etc., an UAV under a mission (M-UAV) often needs assistance from an edge/cloud server that is reachable from the M-UAV’s location. A connection between an M-UAV and edge server can be established via an access point or AP. Therefore, before sharing any sensitive information with the edge server, it is essential for an M-UAV to determine the legitimacy of the selected AP. Recently, some works in this direction indicate that a rogue UAV (R-UAV) can successfully mimic a legitimate AP for intercepting the communication channel. Hence, there should be a robust detection mechanism in place for addressing such a threat scenario. In this paper, considering one of the emerging domains – the Internet of Vehicle (IoV) networks, at first, we show that communication in the IoV networks can get benefit from the presence of M-UAVs. However, as the link between the M-UAV and edge server can be intercepted by an R-UAV, the adversary may access the sensitive information from the IoV networks. Followed by this, we propose a timing-based algorithm for identifying the presence of rogue APs (or R-UAVs) in the channel. The M-UAV executes the timing-based algorithm, and the detection method does not require any auxiliary hardware or any modification to the network protocols for meeting the objective. Supported by an extensive evaluation study, we show that without any rigid restriction on the M-UAV’s speed (e.g., by limiting it to almost static) the proposed approach significantly enhances the detection accuracy (at least by a margin of 29.7% and 16.65%) compared to the state-of-the-art methods.

The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing (MCS) in the industrial Internet. In this paper, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized as a multi-objective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.

Edge computing combing with artificial intelligence (AI) has enabled the timely processing and analysis of streaming data produced by IoT intelligent applications. However, it causes privacy risk due to the data exchanges between local devices and untrusted edge servers. The powerful analytical capability of AI further exacerbates the risks because it can even infer private information from insensitive data. In this paper, we propose a privacy-preserving IoT streaming data analytical framework based on edge computing, called PrivStream, to prevent the untrusted edge server from making sensitive inferences from the IoT streaming data. It utilizes a well-designed deep learning model to filter the sensitive information and combines with differential privacy to protect against the untrusted edge server. The noise is also injected into the framework in the training phase to increase the robustness of PrivStream to differential privacy noise. Taking into account the dynamic and real-time characteristics of streaming data, we realize PrivStream with two types of models to process data segment with fixed length and variable length, respectively, and implement it on a distributed streaming platform to achieve real-time streaming data transmission. We theoretically prove that Privstream satisfies e -differential privacy and experimentally demonstrate that PrivStream has better performance than the state-of-the-art and has acceptable computation and storage overheads.

In recent years, traditional logistics systems are developing towards intelligence based on the Internet of Things (IoT). Sensing devices throughout the logistics network provide strong support for smart logistics. However, due to the insufficient local computing and storage resources of IoT (Internet of things) devices, logistics records with sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this study, we propose a blockchain-assisted secure storage scheme for logistics data. To be specific, this scheme can be briefly divided into two parts. The first part involves data generation and aggregation, session establishing, records encryption and storage, wherein a blockchain network is used to assist the cloud server with data storage, and smart contracts are deployed to provide reliable storage interfaces. In the second part, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process. Also, the stored records can be securely audited by leveraging the deployed blockchain network. Finally, we analyze the security and privacy properties of this scheme and evaluate its performance in terms of computation and communication overhead by developing an experimental platform. The experimental results indicate that the performance of our scheme is acceptable.

The recent evolution of the Internet of Things in the healthcare sector has attained substantial recognition from the government and industry. Healthcare data accumulated from diverse sources are stored by health service providers which is useful for patient diagnosis as well as for research for pivotal analysis. However, healthcare data contains sensitive information of an individual that needs to be protected. The sensitive health information of an individual contains multiple attributes and the correlation of this information may lead to a privacy breach. In more complex scenarios, such type of data is expected to be released periodically and dynamically, the privacy breach of individuals becomes imminent due to the presence of personal data. In this paper, a fuzzy logic-based intelligent privacy-aware algorithm is proposed to protect the individual’s privacy with multiple sensitive attributes in a dynamic data release scenario. Our formal modeling and analysis show that the proposed approach offers a robust privacy guarantee while releasing health-related data. Furthermore, the empirical evaluation exhibits that the proposed model outperforms the state-of-the-art approaches in terms of information loss and query accuracy.

Contact tracing in case of pandemic is becoming an essential mitigation tool for national health services to break infection chains and prevent the virus from spreading further. To support manual tracing, several countries have been developing contact tracing apps that detect nearby mobile phones using Bluetooth. Such data collection raised privacy concerns and several privacy-preserving protocols have been proposed to prevent the leakage of personal and sensitive information. These solutions are mainly divided into two categories using a centralized or a decentralized exposure score computation. However, both approaches depict limitations. This article presents Desire , a novel exposure notification system that leverages the best of centralized and decentralized systems. As opposed to existing contact tracing schemes, Desire leverages Private Encounter Tokens ( Pets ) generated locally on the device that uniquely identify an encounter between two nodes while being private and unlinkable by the server. The role of the server is merely to match PETs generated by diagnosed users with the pets provided by requesting users. Our privacy risk analysis shows that Desire drastically improves privacy against malicious users (i.e., limitation of decentralized systems) and authority (i.e., limitation of centralised systems). We implemented Desire , evaluated it in real condition, and show it feasibility.

With the rapid growth of machine learning and artificial intelligence in medical cloud systems, cloud-aided medical computing has materialized a concrete platform for rapid realization of the service-oriented computing paradigm. However, using machine learning approaches in medical clouds might cause serious privacy leakage risk, for example, it needs to take the patients’ sensitive data as the inputs of the machine learning algorithm. In this article, we propose an efficient and privacy-preserving clinical diagnosis scheme that was performed by an (untrusted and malicious) outsourced cloud platform, which can provide the diagnosis assistance for doctors without leaking any sensitive information of patients and service providers. Concretely, we give the security model of privacy-preserving multiclass support vector machine (SVM) in outsourced medical clouds and design a secure clinical diagnosis scheme with privacy-preserving multiclass predecision and diagnosis. We propose a novel encoding approach to implement the encryption of negative number, and design several security building blocks, such as privacy-preserving decision function computing, privacy-preserving classification, and search of max decision function on encrypted fields, to provide the construction of the secure clinical diagnosis with privacy-preserving multiclass SVM. We give the concrete scheme and provide the experiment on Dermatology datasheet. Security analysis and experimental results indicate that the proposed scheme is efficient and practical in privacy-preserving clinical diagnosis systems.

The Common Vulnerability Scoring System is at the core of vulnerability management for systems of private corporations to highly classified government networks, allowing organizations to prioritize remediation in descending order of risk. With a lack of justification for its underlying formula, inconsistencies in its specification document, and no correlation to exploited vulnerabilities in the wild, it is unable to provide a meaningful metric for describing a vulnerability’s severity, let alone risk. As it stands, this standard compromises the security of America’s most sensitive information systems.

With the development of location-based applications, more and more trajectory data are collected. Trajectory data often contains users’ sensitive information, and direct release it may pose a threat to users’ privacy. Differential privacy, as a privacy preserving method with solid mathematical foundation, has been widely used in trajectory data publishing. However, current trajectory data publishing methods based on differential privacy cannot fully realize the personalized privacy protection. In this paper, an optimal personalized trajectory differential privacy mechanism is proposed. Firstly, by establishing the probabilistic mobility model of trajectories, we cluster the locations to achieve semantic location matching between different trajectories. Based on the semantic similarity, we identify the templet trajectory, and propose a privacy level allocation method based on stay-points and frequent sub-trajectories. Then, according to the location matching results, we can automatically identify the privacy level of all locations. Combined with the optimal location differential privacy mechanism, we disturb the location points on the user’s trajectory before publishing, where different location privacy levels correspond to different privacy budgets. Experiment results on real-world datasets show that our mechanism provides a better tradeoff between privacy protection and data utility compared with traditional differential privacy methods.

Today, cloud storage becomes one of the critical services, because users can easily modify and share data with others in cloud. However, the integrity of shared cloud data is vulnerable to inevitable hardware faults, software failures or human errors. To ensure the integrity of the shared data, some schemes have been designed to allow public verifiers (i.e., third party auditors) to efficiently audit data integrity without retrieving the entire users' data from cloud. Unfortunately, public auditing on the integrity of shared data may reveal data owners' sensitive information to the third party auditor. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at least t group managers to recover a trace key cooperatively, which eliminates the abuse of single-authority power and provides non-frameability. Moreover, our scheme ensures that group users can trace data changes through designated binary tree; and can recover the latest correct data block when the current data block is damaged. In addition, the formal security analysis and experimental results indicate that our scheme is provably secure and efficient.

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user's location such as the user's home address or the user's trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user's location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user's rough location information but cannot obtain the user's exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson-Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of t he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.