In recent years, Cross-Modal Retrieval (CMR), which can retrieve data across types based on query semantics, has become an attractive technology due to the widespread applications of multimedia data. Outsourcing multimedia data to a cloud server is a reliable way to improve the quality of CMR services, but it will also incur potential data privacy leakage issues. Existing schemes for privacy-preserving outsourced data search services are either inapplicable to CMR or limited by efficiency and scalability. To address the above issues, we investigate the problem of Searchable Symmetric Encryption (SSE) for CMR in this paper. Firstly, we formulate the definition of SSE for CMR (namely, SSECMR) and extend the SSE leakage functions to capture the leakage in SSECMR. Then, by constructing distance-computation-free Hamming inverted multi-index, we propose a practical SSECMRconstruction. Specifically, we transform Hamming distance-based range queries into multi-key queries, thereby avoiding computationally expensive comparison operations and enabling efficient Hamming distance queries over encrypted data. Our design supports secure CMR with sub-linear complexity in a single communication roundtrip. Through rigorous security analysis, we demonstrate that our construction can provide adaptive security. Empirical evaluations on real-world datasets demonstrate that SSECMRoutperforms the state-of-the-art scheme in both efficiency and accuracy, and is comparable to plaintext applications. Our code is available at https://github.com/SSE-CMR/SSECMR.

Secure Location-Based Services (LBSs) in mobile cloud have gained widespread attention in the past decade. However, previous works mainly focus on spatial or spatial keyword query services and cannot support temporal filters simultaneously, which limits the service quality in practical applications. To address the above issue, we propose an efficient Keyword-associated Spatio-Temporal Structured Encryption (KSTSE) scheme that allows conjunctive queries according to spatio-temporal range and textual keywords on encrypted data in a mobile cloud. Specifically, we first transform geographic and temporal range queries into a unified encoding existence detection problem by combining S2 encoding and prefix encoding. Next, we build an efficient encrypted existence evaluation construction based on the circular shift and coalesce Bloom filter and symmetric hidden vector encryption. Finally, to support efficient queries on large-scale datasets, we design a hierarchical index tree structure, which can dynamically prune the search space according to the keyword and spatio-temporal range during the query process, reducing the query complexity toO(logN) Rigorous security analysis and performance evaluation show that the proposed KSTSE construction is adaptively secure under reasonable leakages and performs better than state-of-the-art schemes

Electronic medical records (EMRs) are the essential sensitive personal data that is shared between patients and doctors through the semi-trusted E-health cloud. In the real application, multiauthority ciphertext-policy attribute-based searchable encryption (MA-CP-ABSE) is suitable to protect the security of the EMR for it possesses fine-grained access permission, efficient key management, and retrieval function over the encrypted data. However, previously proposed MA-CP-ABSE schemes are commonly restricted by the central authority, which is required by all attribute authorities in some operations like generating users’ secret keys. To deal with this issue, we proposed a decentralized MA-CP-ABSE (DMA-CP-ABSE) scheme. In our scheme, any attribute authority can become an independent authority to generate a secret key, which is no longer controlled by the central authority. Furthermore, a single-keyword search will produce many disrelated search results. For this, we enhance the DMA-CP-ABMSE scheme by implementing a multikeyword search function to improve the search accuracy. Besides, to handle the dynamic changing of access permission, we have designed the attribute revocation methods. Finally, we process the formal security analysis to demonstrate our scheme is secure under the chosen-keyword attack (CKA) and implement experiments to show its efficiency and feasibility.

In recent years, the widely collected spatial-textual data has given rise to numerous applications centered on spatial keyword queries. However, securely providing spatial keyword query services in an outsourcing environment has been challenging. Existing schemes struggle to enable top- k spatial keyword queries on encrypted data while hiding search, access, and volume patterns, which raises concerns about availability and security. To address the above issue, this paper proposes OBIR-tree, a novel index structure for oblivious (provably hides search, access, and volume patterns) top- k spatial keyword queries on encrypted data. As a tight spatial-textual index tailored from the IR-tree and PathORAM, OBIR-tree can support sublinear search without revealing any useful information. Furthermore, we present extension designs to optimize the query latency of the OBIR-tree: (1) combine the OBIR-tree with hardware secure enclaves ( e.g., Intel SGX) to minimize client-server interactions; (2) build a Real/Dummy block Tree (RDT) to reduce the computational cost of oblivious operations within enclaves. Extensive experimental evaluations on real-world datasets demonstrate that the search efficiency of OBIR-tree outperforms state-of-the-art baselines by 25x ~ 723× and is practical for real-world applications.

Over the past decades, Sequence Similarity Query (SSQ) has been widely used in genomic analysis. Several privacy-preserving SSQ schemes have been proposed to protect sensitive genomic data but struggle to balance security and efficiency. This paper proposes a Privacy-preserving Genomic SSQ (PGSSQ) scheme to address the above issue. Specifically, we first design two fundamental privacypreserving genomic matching methods, Edit-distance Threshold Match (ETM) and Dual-Threshold Match (DTM), to support approximate editdistance based threshold SSQ matches and range-constrained SSQ matches on encrypted high-dimensional genomic sequences, respectively. Then, we present a genetic index structure called Genomic Evaluation Tree (GE-Tree) based on the ETM and DTM. GE-Tree enables dynamic pruning of query paths without disclosing any genomic information from encrypted nodes, thereby supporting privacy-preserving SSQ on encrypted genomic data with sublinear computational complexity. Security analysis proves that PGSSQ is secure under selective chosenplaintext attacks. Experiments on a real-world dataset show that PGSSQ is efficient compared to state-of-the-art schemes.

Cloud-based outsourced Location-based services significantly impact various aspects of daily life but also raise security concerns. Existing secure retrieval schemes for spatiotemporal data exhibit significant shortcomings regarding dynamic updates; they either compromise privacy through information leakage during updates (lacking forward security) or incur excessively high update costs, hindering practical application. To address these limitations, we first propose a basic filter-based spatio-temporal range query scheme Trinity-I that supports lowcost dynamic updates and automatic expansion. Furthermore, to improve security, reduce storage cost, and false positives, we propose a forward secure and verifiable scheme Trinity-II that simultaneously minimizes storage overhead. Formal security analysis demonstrates that both Trinity-I and Trinity-II achieve Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA). Finally, extensive experiments demonstrate that our design Trinity-II significantly reduces storage requirements by 80%, enables data retrieval at the 1 million-record level in just 0.01 seconds, and achieves 10× update efficiency than state-of-art.

FL is vulnerable to model poisoning attacks due to the invisibility of local data and the decentralized nature of FL training. The adversary attempts to maliciously manipulate local model gradients to compromise the global model (i.e., victim model). Commonly-studied model poisoning attacks heavily depend on accessing additional knowledge, such as local data and the aggregation algorithm from the victim model, which easily encounter practical obstacles due to limited adversarial knowledge. In this paper, we first reveal that aggregated gradients in FL can serve as an attack carrier, exposing the latent knowledge of the victim model. In particular, we propose a data-free model poisoning attack named FedGhost, which aims to redirect the training objective of FL towards the adversary’s objective without any auxiliary information. In FedGhost, we design a black-box adaptive optimization algorithm to dynamically adjust the perturbation factor for malicious gradients, maximizing the poisoning impact of FL. Experimental results on five datasets in IID and Non-IID FL settings demonstrate that FedGhost achieves the highest attack success rate, outperforming other state-of-the-art model poisoning attacks by more than$10\%-60\%$.

Spatio-temporal query services have been playing an increasingly important role in people’s daily lives. While outsourcing such services to third parties (e.g., cloud servers) offers considerable benefits, it raises data privacy issues. However, no existing work can fully support secure and efficient spatio-temporal queries in outsourcing environments. In this paper, we investigate the problem of Spatio-Temporal Range queries over Encrypted databases (STRE). Firstly, we design a new index structure, called Hilbert Hierarchical Prefix Bloom tree (H2PB-tree), which reduces the search complexity of spatio-temporal range query to sublinear. Then, we build an efficient STRE construction called E-STRE in the single-cloud model based on H2PB-tree and symmetric hidden-vector encryption. Moreover, we perform sufficient security analysis and experimental test, and the results show E-STRE outperforms prior arts in terms of performance while guaranteeing data security. Compared with the prior arts presented under the single-cloud model, our construction reduces the query delay by at least 18×.

GPS-enabled Internet of Things devices, which can obtain the location and temporal information of installed objects to promote location-based service, are completely changing our lives. In recent years, the confidentiality and privacy of personal data have attracted widespread attention, especially when outsourcing to third-party providers. To achieve both the confidentiality and availability of outsourced data, various dynamic searchable symmetric encryption (DSSE) schemes have been proposed. However, existing solutions are limited either in terms of security or efficiency. To address this challenge, we propose a secure and efficient search over encrypted spatiotemporal data (SES-ESTD) scheme that utilizes constrained pseudo-random function and enhanced asymmetric scalar-product preserving encryption. Our scheme not only achieves high retrieval efficiency but also ensures forward security and content privacy. We provide a formal security analysis to prove SES-ESTD is forward secure and content private. Furthermore, extensive experiments indicate that SES-ESTD incurs lower computation and storage overheads compared to other schemes. Most importantly, SES-ESTD achieves millisecond-level retrieval for millions of data points and provides a retrieval speed that is 2.85 times faster than existing forward secure spatiotemporal DSSE schemes.

Image search is a hot topic, which has played a significant role in various Internet of Things (IoT) applications, such as disease diagnosis, face recognition, and fingerprint recognition. Meanwhile, the proliferation of images has led image owners to outsource images to the cloud for reducing local storage and computation burdens. Therefore, image search without compromising privacy over cloud has received considerable attention and extensively explored in the literature. Many Bloom filter (BF)-based schemes have been put forth in past years, however, most of them suffer from high storage overhead, low false positive rate, and even expose the values in BF. To solve these challenges, in this article, we first design a merged and repeated indistinguishable BF (MRIBF) index structure, which can reduce the storage overhead and achieve adaptive security with a low false positive rate. Then, with the MRIBF, we propose a secure and efficient BF-based image search (BFIS) scheme to achieve a faster-than-linear and more accurate search. Detailed theoretical analysis shows that our scheme is really accurate and secure. Extensive experiments demonstrate that our scheme is indeed efficient and feasible.

Over the past decade, genomic data has grown exponentially and is widely used in promising medical and health-related applications, which opens up new opportunities for the field of medicine. Similar patients query (SPQ), which can help physicians formulate an optimal therapy, is one of such popular applications. Despite its popularity, since human genomes are usually highly sensitive, a series of policies have been launched by the government to strictly control its acquisitions and utilization. Thus, how to prevent privacy disclosure becomes of great importance to the flourish of SPQ services. In this article, aiming at the above challenge, we first design a novel genetic BK-tree (GBK-tree) for a genomic database. Then, combined with a random sorting mechanism and some existing encryption techniques, we propose an efficient and privacy-preserving similar patients query scheme over encrypted cloud data, named CASPER. With CASPER, a medical institution can securely outsource its private genomic database to a cloud server, and physicians can request SPQ services from the cloud server while keeping her/his query secret. Detailed security analysis shows that CASPER can preserve privacy in the presence of different threats. Furthermore, extensive performance evaluations demonstrate the high accuracy and efficiency of our proposed scheme.

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis proves that our construction can achieve not only forward/backward privacy but content privacy as well, which can resist the most existing leakage-abuse attacks. Evaluation results using real-world datasets demonstrate the efficiency and feasibility of our construction. Comparative analysis reveals that our construction outperforms state-of-the-art schemes in terms of privacy and performance, e.g., our construction is 175x faster than existing schemes with only 51% server storage cost.

Cloud computing, which provides a brand-new service model, has become an important infrastructure in the information age, and has been widely used in numerous fields. The Key-Policy Attribute-Based Encryption (KP-ABE) scheme allows the encrypted data with fine-grained access control in the cloud environment. However, achieving large-scale user revocation in the application scenario of KP-ABE becomes one of the thorny problems. Furthermore, the computation and communication costs of the previous user revocation schemes were generally high, especially when a large number of users were revoked. To address these problems, an enhanced high-performance user-revocable KP-ABE scheme combined with the puncture method was proposed. In this article, the user could be revoked by the fine-grained restriction policy. When revoking the user, the cloud would run the puncture algorithm to embed the restriction policy defined by the data owner into the ciphertext. This method could effectively omit the re-encryption and key updating processes, by which the computation and communication overhead of the user revocation are efficiently reduced, and the user revocation becomes more flexible and efficient. Moreover, the Chosen-Plaintext Attack (CPA) security proof and extensive simulation results demonstrate the reliability and efficiency of the proposed scheme for user revocation in a cloud environment.

Spatial keyword queries have attracted much attention over the past decade due to the popularity of location-based services and social networks, which brings great economic benefits. Geo-textual data are encrypted-and-delegated to public clouds for efficient management and utilization while preventing potential data leakage. However, it is still challenging to solve securerangespatialkeyword queries on encrypted data since existing works are either vulnerable or inefficient. In this paper, a secure hybrid index is built to implement efficient filtering, by embedding nodes’ paths in a novel symmetrical kd-tree into inverted indexes and employing only lightweight cryptographic techniques. A concrete scheme RASK is constructed on the secure index by utilizing only a little storage and computing resources of clients. Furthermore, RASK+ is proposed based on secure virtual technology by migrating all storage burdens from clients to public clouds. Both schemes are theoretically proved to beindistinguishable under adaptive chosen keyword attacks(IND-CKA2). Through experimental evaluations on three real datasets within consistent environments, both schemes reduce the response time by about 50%-80% compared to state-of-the-art solutions (i.e., SKSE, LSKQ, etc.). The storage overheads for the cloud are also reduced by about 0.5-2 orders of magnitude.

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.

With the development of machine learning, it is popular that mobile users can submit individual symptoms at any time anywhere for medical diagnosis. Edge computing is frequently adopted to reduce transmission latency for real-time diagnosis service. However, the data-driven machine learning, which requires to build a diagnosis model over vast amounts of medical data, inevitably leaks the privacy of medical data. It is necessary to provide privacy preservation. To solve above challenging issues, in this article, we design a lightweight privacy-preserving medical diagnosis mechanism on edge, called LPME. Our LPME redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to remove amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edge. In addition, LPME provides secure diagnosis on edge with privacy preservation for private and timely diagnosis. Our security analysis and experimental evaluation indicates the security, effectiveness, and efficiency of LPME.

With the development of Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining technique. However, sensitive data privacy is still a major concern. In this article, we propose practical techniques for searching and making online pre-diagnosis over encrypted data. First, we propose a new Diverse Keyword Searchable Encryption (DKSE) scheme which supports multi-dimension digital vectors range query and textual multi-keyword ranked search to gain a broad range of applications in practice. In addition, a framework called PRIDO based on the DKSE is designed to protect patients’ personal data in data mining and online pre-diagnosis. According to the PRIDO framework, we achieve privacy-preserving naïve Bayesian and decision tree classifiers and discuss its potential applications in actual deployments. Security analysis proves that patients’ data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in the diverse keyword search, data mining, and disease pre-diagnosis, respectively.

Recently, spatial keyword query services have been widely deployed in real-life applications, such as location-based services and social networking. Several privacypreserving spatial keyword queries solutions were proposed to guarantee data security and query privacy on outsourced data. However, those solutions are either based on broken cryptographic tools or support a single query type, and hence cannot meet the security and functionality requirements in practical applications. In this paper, we propose a Secure Spatial Keyword Queries (SSKQ) construction supporting expressive query types. Specifically, we present a secure index structure for spatial-textual data based on the encrypted Quadtree and Bloom filter, which can prune the index tree dynamically and only reveal the files associated with a set of keywords. The security analysis and the experiments conducted on real-world datasets demonstrate the security and performance of our construction.

In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.

With the development of the Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining techniques. In MHMN, patients’ personal data are collected by sensors per second and uploaded to the cloud server as multi-dimension vectors, cloud server stores the personal data as well as sends monitoring information to the hospital when the real-time data is abnormal. Hospital users (i.e., doctors, etc.) may query some samples which contain certain textual keywords or digital keywords in certain ranges for disease diagnosis or medical research. For example, a certain hospital user may query all samples with textual keywords ‘cancer; diabetes’ and digital vectors ‘age’ ∈ [30,50], ‘blood sugar’ ∈ [4,8], ‘heart rhythm’ ∈ [70,80]. Besides, the potential value of massive medical data has attracted considerable interests recently, for example, valuable results in diagnosis model can be yield with large-scale aggregation analysis of personal medical data. The cloud server can build a diagnosis model using data mining technology over massive data, so that hospital users or pre-diagnosis users upload medical data (i.e., age, blood pressure, blood sugar, etc.) to the cloud for diagnosis.

With the advancement of mobile Internet, cloud computing, and smart sensing devices, location-based services (LBSs) have become more and more indispensable in the Internet-of-Things (IoT)-based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This article proposes two secure dynamic spatial keyword queries (SDSKQs) constructions that support expressive query types and dynamic update. First, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure forspatio-textualdata, named encrypted textual signature quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world data sets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.

Structured Encryption (STE), which allows a server to provide secure search services on encrypted data structures, has been widely investigated in recent years. To meet expressive search requirements in practical applications, a large number of STE constructions have been proposed either on textual keywords or spatial data. However, STE on spatio-textual data, which are widely used in location-based services, has not been fully investigated. In this paper, we formally define the notion of Spatial Keyword Structured Encryption (SKSE) and propose several concrete SKSE constructions with various efficiency-security trade-offs. Firstly, we propose a basic construction with linear search complexity, which only leaks the private files matching both spatial range query and all query keywords. Then, to improve the search efficiency on large-scale datasets, we present a novel tree-based construction with sub-linear search complexity. Finally, we introduce a post-validation approach to remove false positives and further improve storage and search performance. Our constructions are general in the sense that they can be constructed from any hidden vector encryption schemes, including public-key setting and symmetric-key setting, which can meet different sharing requirements. Our rigorous security analysis and comprehensive performance evaluation demonstrate that the proposed constructions are secure and outperform the start-of-the-art solutions.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.

The traditional privacy-preserving image retrieval schemes not only bring large computational and communication overhead but also cannot well protect the image and query privacy in multi-user scenarios. To solve the above problems, we first propose a basic privacy-preserving content-based image retrieval (CBIR) framework which significantly reduces storage and communication overhead compared with the previous works. Furthermore, we design a new efficient key conversion protocol to support unshared key multi-owner multi-user image retrieval without losing search precision. Moreover, our framework supports unbounded attributes and can trace malicious users according to leaked secret keys, which significantly improve the usability of multi-source data sharing. Strict security analysis shows that the user privacy and outsourced data security can be guaranteed during the image retrieval process, and the performance analysis using real-world dataset shows that the proposed image retrieval framework is efficient and feasible for practical applications.

With the development of Mobile Healthcare Monitoring Network (MHMN), patients' personal data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enable clinicians to make proper decisions by utilizing data mining technique. However, the sensitive data privacy is still a major concern. In this paper, we first propose an Efficient Privacy-preserving Sensor data Monitoring and online Diagnosis (EPSMD) system for outsourced computing, then furnish an improved Multidimensional Range Query Technique (MRQT) to gain a broad range of applications in practice. In addition, a privacy-preserving naive Bayesian classifier based on MRQT is designed to protect patients' data in data mining and online diagnosis efficiently. Security analysis proves that patients' data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in data monitoring and disease pre-diagnosis, respectively.