Outsourcing Environment Research Articles

Secure decision tree classification with decentralized authorization and access control

Outsourcing decision tree classification services to the cloud is highly beneficial, yet raises critical privacy problems. In order to preserve data privacy, data owners may choose to upload encrypted data rather than raw data to the classification services. However, these solutions adopted today for encrypted data classification not only fall short in system flexibility and scalability, but also face the single point of failure problem. In this paper, we design, implement, and evaluate a secure decision tree classification scheme that allows decentralized authorization and access control service (SDTC-DAAC). Firstly, we propose a new framework that decouples data encryption and data computation logic to achieve the separation of data storage and computation, which significantly improves upon the flexibility and effectiveness, thus achieving cross-system compatibility requirements. Secondly, we present an end-to-end encrypted access control mechanism which enables authorized users from different parties to participate in calculations together. Finally, we further devise a scheme which serves decentralized storage service of data access control policies and access authorization without trusted intermediaries. Extensive property and performance analysis shows that SDTC-DAAC is effectiveness, as well as satisfying the security requirements for data privacy in an outsourcing environment.

Survey on Secure Keyword Search over Outsourced Data: From Cloud to Blockchain-assisted Architecture

Secure keyword search is a prevailing search service offered in outsourced environments. However, with the increasingly severe security vulnerabilities of conventional centralized outsourcing, the architecture of secure keyword search, with searchable encryption (SE) as the underlying technique, has recently shifted from cloud-centered models to blockchain-assisted models. Existing surveys commonly fail to capture such an evolution and the corresponding benefits. What on earth does blockchain bring about and what are the unexplored challenges? This survey provides a systematic review of secure keyword search over outsourced data from cloud to blockchain-assisted architectures. We propose a taxonomy assorting present studies, depending on whether cloud/blockchain and data sharing are included, in which blockchain-assisted architecture is further divided into blockchain-side and cloud-side keyword search, respectively. Technically, we conclude five types of representative SE techniques with fitting architectures, either cryptographic-based or hardware-dependent. Notably, we propose comprehensive methodologies to select relevant papers, discuss, and compare existing schemes regarding functionalities, security, efficiency, and fairness (up to 21 compared items). Finally, open issues and potential research directions are identified for future work. We aspire to help pave the way for addressing the theoretical and empirical aspects of secure keyword search and full-fledged real-world implementation of blockchain-based keyword search applications.

PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced Environments

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.

The impact of regulatory requirements on German financial institutions’ outsourcing arrangements

PurposeThe purpose of this paper is to analyse and investigate how intensified regulatory requirements related to outsourcing have influenced and changed the outsourcing activities of German financial institutions.Design/methodology/approachThe study involved interviewing 11 outsourcing experts in the German financial sector, including four of the five largest banks in Germany. In coding and analysing the collected data, this study adopted the approach of a qualitative content analysis framework.FindingsThe study found that the revised legal requirements have had a significant and potentially negative impact on the efficiency of outsourcing, leading to a necessity for German financial institutions to internally realign their outsourcing managements. The study further revealed practical realigned methods German financial institutions executed to meet the legal requirements.Originality/valueThe impact, meaning and relevance of legal requirements in the outsourcing environment of German financial institutions has been relatively under-researched from a qualitative perspective and focused on other primary fields of investigation like outsourcing decisions and outcomes. This study has, by adopting a qualitative approach, addressed the identified gap by providing first-hand insights and new knowledge.

Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems

In recent years, edge-related smart computing is the way to process and store data via outsourcing environments. Security is the main progressive concept in smart-related Internet of Things (IoT) software denied networks to share and increase the scalability and efficiency in data storage. Because of the rapid growth of different smart services, different security-related problems may appear in resource processing and sharing data in real-time computing systems. To increase the reliability in secure data storage and satisfy the basic requirements related to IoT-related smart computing networks. So propose and implement a Novel Artificial Intelligence based Blockchain Secure Model (NAIBSM) to provide efficient secure data storage in IoT-related smart computing systems. This model flexibly captures each user authentication for the detection of different user-related attacks (i.e. distributed denial-of-service (DDOS)) in the storage of data via applying Artificial Intelligence (AI) calculation method i.e. Leverage Bat algorithm to explore complex features. Build a blockchain at the server side to provide secure communication and reliability of storing data on the terminal of IoT. This approach provides random hash values to each data to ensure blockchain for the integrity of data and uses a weight-based data storage procedure to arrange/store and classify data to each user with secure and unsecured storage in smart computing networks. The experimental results of the proposed model are to explore complex security features and ensure the performance of secure authentication to each user and better security, accuracy, and low communication overhead in IoT-related smart computing data storage and sharing systems.

Fully privacy-preserving location recommendation in outsourced environments

Currently, location-based services (LBS) have been widely used in real-world settings, including restaurant and travel recommendations. To reduce workload and improve query efficiency, a service provider usually outsources its services to a powerful cloud server. However, the service provider’s database and users’ queries always contain sensitive information, so their leakage to the cloud may raise serious privacy concerns. Although some existing schemes have been proposed to address the privacy problems, they are impractical in real-world LBS due to some issues in privacy, accuracy, or heavy computation costs for query users. In order to overcome these problems, we propose a fully privacy-preserving location recommendation scheme that supports multi-attribute queries and returns accurate results based on the recommendation condition. Specifically, based on the Paillier cryptosystem, we first propose a secure equal test (SET) protocol to check whether two encrypted values are equal. Second, with our proposed protocols, we develop a privacy-preserving location recommendation scheme without revealing anything about the service provider or query users. Finally, we analyze the security of our scheme in the semi-honest model and show that the privacy of the service provider and query users is well protected. Meanwhile, we evaluate the performance of our scheme using synthetic datasets. The experimental results demonstrate that our proposed scheme is practical in real-world applications.

GLOBAL OUTSOURCING DYNAMICS: A COMPREHENSIVE ANALYSIS OF FACTORS DRIVING BUSINESS PROCESS OUTSOURCING (BPO) TO INDIA AND BANGLADESH, WITH A FOCUS ON INFLUENCING FACTORS FOR LEADING COMPANIES IN INDONESIA AND SINGAPORE

This study examines the complexities of offshore outsourcing undertaken by important corporations in Singapore and Indonesia, whereby they engage in subcontracting their activities to Business Process Outsourcing (BPO) firms located in India and Bangladesh. The objective of this study is to comprehensively examine the various factors that drive, shape, and impact the strategic decision-making process. This study utilises a content analysis approach to thoroughly examine textual data obtained from annual reports, press announcements, business statements, and industry publications. This systematic approach facilitates the provision of qualitative insights pertaining to the determinants of offshore outsourcing decisions, hence contributing to a thorough comprehension of the complex offshore outsourcing environment. The primary conclusions of this study highlight the prevailing rationale behind offshore outsourcing, which is the pursuit of cost effectiveness. Prominent corporations place a high emphasis on the reduction of operational expenses by leveraging the cost advantages associated with lower labour prices in India and Bangladesh. Concurrently, they endeavour to get entry to a proficient pool of talent, acknowledging the presence of specialised talents and domain expertise within certain geographical areas. Additionally, the study emphasises the growing tendency to prioritise core capabilities, resulting in improved productivity and operational efficiency. Furthermore, there is a growing emphasis on the implementation of efficient business continuity planning and risk mitigation techniques, which serve as crucial measures to counteract unexpected interruptions by offering geographic diversity and redundancy. The firms employ many tactics in offshore outsourcing that involve cost optimisation, talent recruiting, risk management, and quality control. Notwithstanding the benefits, overseas outsourcing poses various problems, encompassing cultural and linguistic hurdles, ongoing quality control and data security apprehensions, as well as intricate legal and ethical deliberations. The future implications underscore the need for a balanced approach that effectively reconciles considerations of both cost and quality. In order to effectively navigate the intricate offshore outsourcing landscape, it is advisable to employ strategies that handle cultural and language obstacles, as well as demonstrate vigilance in resolving compliance and ethical problems. This study provides practical recommendations for organisations, while also acknowledging its limitations, particularly the reliance on textual data analysis, which may not fully capture real-time nuances. This study highlights the wider economic and social consequences associated with offshore outsourcing choices, which have an impact on employment markets and corporate strategies in both the nations that outsource and those who receive the outsourced work. This research makes a valuable contribution to the current knowledge of offshore outsourcing by highlighting its varied nature within the context of the global business landscape.

GLOBAL OUTSOURCING DYNAMICS: A COMPREHENSIVE ANALYSIS OF FACTORS DRIVING BUSINESS PROCESS OUTSOURCING (BPO) TO INDIA AND BANGLADESH, WITH A FOCUS ON INFLUENCING FACTORS FOR LEADING COMPANIES IN INDONESIA AND SINGAPORE

This study examines the complexities of offshore outsourcing undertaken by important corporations in Singapore and Indonesia, whereby they engage in subcontracting their activities to Business Process Outsourcing (BPO) firms located in India and Bangladesh. The objective of this study is to comprehensively examine the various factors that drive, shape, and impact the strategic decision-making process. This study utilises a content analysis approach to thoroughly examine textual data obtained from annual reports, press announcements, business statements, and industry publications. This systematic approach facilitates the provision of qualitative insights pertaining to the determinants of offshore outsourcing decisions, hence contributing to a thorough comprehension of the complex offshore outsourcing environment. The primary conclusions of this study highlight the prevailing rationale behind offshore outsourcing, which is the pursuit of cost effectiveness. Prominent corporations place a high emphasis on the reduction of operational expenses by leveraging the cost advantages associated with lower labour prices in India and Bangladesh. Concurrently, they endeavour to get entry to a proficient pool of talent, acknowledging the presence of specialised talents and domain expertise within certain geographical areas. Additionally, the study emphasises the growing tendency to prioritise core capabilities, resulting in improved productivity and operational efficiency. Furthermore, there is a growing emphasis on the implementation of efficient business continuity planning and risk mitigation techniques, which serve as crucial measures to counteract unexpected interruptions by offering geographic diversity and redundancy. The firms employ many tactics in offshore outsourcing that involve cost optimisation, talent recruiting, risk management, and quality control. Notwithstanding the benefits, overseas outsourcing poses various problems, encompassing cultural and linguistic hurdles, ongoing quality control and data security apprehensions, as well as intricate legal and ethical deliberations. The future implications underscore the need for a balanced approach that effectively reconciles considerations of both cost and quality. In order to effectively navigate the intricate offshore outsourcing landscape, it is advisable to employ strategies that handle cultural and language obstacles, as well as demonstrate vigilance in resolving compliance and ethical problems. This study provides practical recommendations for organisations, while also acknowledging its limitations, particularly the reliance on textual data analysis, which may not fully capture real-time nuances. This study highlights the wider economic and social consequences associated with offshore outsourcing choices, which have an impact on employment markets and corporate strategies in both the nations that outsource and those who receive the outsourced work. This research makes a valuable contribution to the current knowledge of offshore outsourcing by highlighting its varied nature within the context of the global business landscape.

Parallelly Running and Privacy-Preserving k-Nearest Neighbor Classification in Outsourced Cloud Computing Environments

Classification is used in various areas where k-nearest neighbor classification is the most popular as it produces efficient results. Cloud computing with powerful resources is one reliable option for handling large-scale data efficiently, but many companies are reluctant to outsource data due to privacy concerns. This paper aims to implement a privacy-preserving k-nearest neighbor classification (PkNC) in an outsourced environment. Existing work proposed a secure protocol (SkLE/SkSE) to compute k data with the largest/smallest value privately, but this work discloses information. Moreover, SkLE/SkSE requires a secure comparison protocol, and the existing protocols also contain information disclosure problems. In this paper, we propose a new secure comparison and SkLE/SkSE protocols to solve the abovementioned information disclosure problems and implement PkNC with these novel protocols. Our proposed protocols disclose no information and we prove the security formally. Then, through extensive experiments, we demonstrate that the PkNC applying the proposed protocols is also efficient. Especially, the PkNC is suitable for big data analysis to handle large amounts of data, since our SkLE/SkSE is executed for each dataset in parallel. Although the proposed protocols do require efficiency sacrifices to improve security, the running time of our PkNC is still significantly more efficient compared with previously proposed PkNCs.

Practical Privacy Preserving-Aided Disease Diagnosis with Multiclass SVM in an Outsourced Environment

With the rapid development of cloud computing and machine learning, using outsourced stored data and machine learning model for training and online-aided disease diagnosis has a great application prospect. However, training and diagnosis in an outsourced environment will cause serious challenges to the privacy of data. At present, many scholars have proposed privacy preserving machine learning schemes and made a lot of progress, but there are still great challenges in security and low client load. In this paper, we propose a complete privacy preserving outsourced multiclass SVM training and aided disease diagnosis scheme. We design some efficient basic operation algorithms for encrypted data. Then, we design an efficient and privacy preserving SVM model training protocol using the basic operation algorithms. We propose a secure maximum finding algorithm and secure comparison algorithm. Then, we design an efficient online-aided disease diagnosis scheme based on the BFV cryptosystem and blinding technique. Detailed security analysis proves that our scheme can protect the privacy of each participant. The experimental results illustrate that our proposed scheme significantly reduces the computation overhead compared with the previous similar works. Our proposed scheme completes most of the operations of aided disease diagnosis by the cloud servers and the client only needs to complete a small amount of encryption and decryption operations. The overall computation overhead is 0.175 s, and the efficiency of online aided disease diagnosis is improved by 85.4%. At the same time, our proposed scheme provides multiclass diagnosis results, which can better assist doctors in their treatment.

Group data freshness scheme for outsourced data in distributed systems

Data freshness ensures accessing recent data that could help in achieving high business values and providing effective customer service. Group data freshness is a challenging aspect in a distributed outsourced environment, as stale data among different entities may mislead the business goal of the system. Generally, three-party data outsourcing model is found in practice: users, data owner, and cloud service provider. The users require to register with the data owner to access data files directly from the cloud service provider. A scheme verifying the freshness of whole outsourced data of its readers is called the group data freshness auditing scheme (GDFAS). Existing GDFASs focus on a probabilistic guarantee and require high computational cost at the data owner. In this paper, an efficient group data freshness auditing scheme is proposed, where the data owner does auditing in a distributed system with the help of the system users. As the data owner is not directly involved in their user’s data access, it needs mechanisms such as auditing data through an additional third-party to ensure the data is fresh. However, the third-party data storage service provider may not be fully trusted by a data owner. In such context, auditing data with respect to its freshness property without involving additional third-party storage service is challenging, but would be more effective in terms of the system’s performance and efficacy. The proposed GDFAS provides real-time data freshness verification using Merkle hash trees. In comparison to the existing scheme, it takes less computational cost at the data owner without involving any third party and less communication cost between the data owner and the service provider. The proposed GDFAS is implemented on the AWS cloud and the auditing cost at the data owner is experimentally evaluated. The proposed GDFAS is analyzed and compared with the relevant existing scheme and is found that the proposed GDFAS outperforms other schemes with respect to its security and efficiency.

Developing offshore outsourcing practices in a global selective outsourcing environment – the IT supplier’s viewpoint

Currently, internal IT organizations use outsourcing and offshore arrangements to achieve cost savings and gain access to new capabilities. It was found that suppliers’ personnel at the operational level can face challenges with internalizing their operations based on the agreed outsourcing practices and transferred responsibilities. This study gives voice to the supplier and studies the impact of offshore outsourcing operation development activities. The internal IT unit from Nokia Devices selectively outsourced global IT service activities and responsibilities to the IT supplier. The outsourced activities were implemented by offshore centers in India and China. It was found that the global selective outsourcing environment (GSOE) did not provide a solution to all of their expectations, and new unexpected challenges occurred. Several practices, communication and information sharing, and behavior-related lessons learned items were identified. It was found that the GSOE operation needs to be developed and implemented in an agile and incremental manner, instead of a singular implementation approach. Also, the globally distributed teams’ group dynamics critically impacted on the teams’ ability to work. The lessons learned items and recommendations can be utilized by other companies during their mode-of-operation development.

Examining interaction among supplier selection strategies in an outsourcing environment using ISM and fuzzy logic approach

Examining interaction among supplier selection strategies in an outsourcing environment using ISM and fuzzy logic approach

Privacy-Preserving Image Watermark Embedding Method Based on Edge Computing

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent.

Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment

As a commonly used algorithm in data mining, clustering has been widely applied in many fields, such as machine learning, information retrieval, and pattern recognition. In reality, data to be analyzed are often distributed to multiple parties. Moreover, the rapidly increasing data volume puts heavy computing pressure on data owners. Thus, data owners tend to outsource their own data to cloud servers and obtain data analysis results for the federated data. However, the existing privacy-preserving outsourced k -means schemes cannot verify whether participants share consistent data. Considering the scenarios with multiple data owners and sensitive information security in an outsourced environment, we propose a verifiable privacy-preserving federated k -means clustering scheme. In this article, cloud servers and participants perform k -means clustering algorithm over encrypted data without exposing private data and intermediate results in each iteration. In particular, our scheme can verify the shares from participants when updating the cluster centers based on secret sharing, hash function and blockchain, so that our scheme can resist inconsistent share attacks by malicious participants. Finally, the security and experimental analysis are carried out to show that our scheme can protect private data and get high-accuracy clustering results.

Personality Characteristic-based Enhanced Software Testing Levels for Crowd Outsourcing Environment

Personality Characteristic-based Enhanced Software Testing Levels for Crowd Outsourcing Environment

Green rules and green tape: Streamlining the environmental review for transportation projects

• Streamlining rules and procedures using green tape improves performance. • Programmatic Categorical Exclusions create substantial time savings for infrastructure project delivery. • Streamlining project sub-disciplines creates performance gains for the entire project. • Green tape theory can be applied to diverse project teams in the outsourced environment. The Georgia Department of Transportation (GDOT) implemented a programmatic agreement as green tape to streamline environmental review and decrease project durations. We investigate the implementation and outcomes associated with this streamlining initiative to examine whether it functions as green tape and investigate whether it improves performance by reducing project durations. We use a mixed-methods approach, using qualitative analysis to explore green tape perceptions of project team members and a quasi-experimental, instrumental variable approach to estimate the time-savings the created for a sample of 397 infrastructure projects. Our results reveal the streamlining initiative operates as green tape and demonstrate the substantial performance gains green tape can yield. These findings suggest that streamlining one project discipline impacts performance across the entire project, creating substantial time savings. Furthermore, they demonstrate how green tape functions in diverse project teams and can be applied in public agencies to create performance gains in infrastructure project delivery.

Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

Abstract We study the problem of privacy-preserving approximate kNN search in an outsourced environment — the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious map. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. We implement, evaluate efficiency, and compare the performance of two concrete schemes based on an oblivious AVL tree and an oblivious BSkiplist.

Uncover the cover to recover the hidden secret - A separable reversible data hiding framework

The volatile development in the multimedia cognitive content is changing the global set-up towards a cloud-based architecture which is helped us with a massive amount of computer storage and the highest computational platform. Cost-saving and elasticity of services will be provided by progressive cloud computing technology for users. With the advancement in multimedia technology, the data owners outsource their private multimedia data on the hybrid cloud. Meantime the cloud servers also carry out some highly computationally expensive tasks. Nevertheless, there is an opportunity for security infracts possible in the public cloud environment. It makes an alarm for a cloud environment in security aspects. Before outsourcing multimedia data, an encryption technique is needed for safeguarding against several attacks. But performing the same is a significant challenge. A new research area was recently awakened on privacy-preserving Reversible Data Hiding (RDH) especially for multimedia data over the outsourced environment. A novel RDH for an encrypted image was proposed in this paper by using the (Most Significant Bit) MSB difference of the pixel value. By using this method, any third-party people can embed the ciphertext in the cipher image without the knowledge of the cover and secret. A person with decryption keys can get back the secret and the cover without any loss. The proposed work achieves the embedding capacity up to 1 bpp (bits per pixel) with the encryption quality of near-zero correlation and uniform histogram. The decrypted image is also retrieved with infinite Peak Signal to Noise Ratio (PSNR), unit Structural Similarity Index Metric (SSIM) and zero Bit Error Rate (BER).

Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Cyber-physical-social systems (CPSS) are revolutionizing the relationships between humans, computers, and things. Outsourcing computation to the cloud can offer resources-constrained enterprises and consumers sustainable computing in CPSS. However, ensuring the security of data in such an outsourced environment remains a research challenge. Principal eigentensor computation has emerged as a powerful tool dealing with multidimensional cyber-physical-social systems data. In this paper, we present two novel secure principal eigentensor computation (SPEC) schemes for sustainable CPSS. To the best of our knowledge, this is the first effort to address SPEC over encrypted data in the cloud without the interaction need between multiple users and cloud. More specifically, we leverage cloud server and trusted hardware component to design a collaborative cloud model. Using the model, we propose (1) a basic SPEC scheme based on homomorphic computing and (2) an efficient SPEC scheme that combines the advantages of homomorphic computing and garbled circuits, and exploits packing technology to reduce computational cost. Finally, we theoretically and empirically analyze the security and efficiency of our SPEC schemes. Findings demonstrate that the proposed schemes provide a secure and efficient way of outsourcing computation for CPSS. In addition, from the cloud user's perspective, our proposal is lightweight.