With the fast development of e-commerce, there is a higher demand for timely delivery. Logistic companies want to send receivers a more accurate arrival prediction to improve customer satisfaction and lower customer retention costs. One approach is to share (near) real-time location data with recipients, but this also introduces privacy and security issues such as malicious tracking and theft. In this paper, we propose a privacy-preserving real-time location sharing system including (1) a differential privacy based location publishing method and (2) location sharing protocols for both centralized and decentralized platforms. Different from existing location perturbation solutions which only consider privacy in theory, our location publishing method is based on a real map and different privacy levels for recipients. Our analyses and proofs show that the proposed location publishing method provides better privacy protection than existing works under real maps against possible attacks. We also provide a detailed analysis of the choice of the privacy parameter and their impact on the suggested noisy location outputs. The experimental results demonstrate that our proposed method is feasible for both centralized and decentralized systems and can provide more precise arrival prediction than using time slots in current delivery systems.

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How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.@en

With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service. Information leakage during transportation is an important reason for theft since thieves can identify which truck is the target that contains the valuable products. In this paper, we address the privacy and security issues in bin-packing, which is an algorithm used in delivery centers to determine which packages should be loaded together to a certain truck. Data such as the weight of the packages is needed when assigning items into trucks, which can be called bins. However, the information is sensitive and can be used to identify the contents in the package. To provide security and privacy during bin-packing, we propose two different privacy-preserving data publishing methods. Both approaches use differential privacy (DP) to hide the existence of any specific package to prevent it from being identified by malicious users. The first approach combines differential privacy with k-anonymity, and the other one applies clustering before differential privacy. Our extensive analyses and experimental results clearly show that our proposed approaches have better privacy guarantees, better efficiency, and better performance than the existing works that use either differential privacy or k-anonymity.@en

Genome sequencing has rapidly advanced in the last decade, making it easier for anyone to obtain digital genomes at low costs from companies such as Helix, MyHeritage, and 23andMe. Companies now offer their services in a direct-to-consumer (DTC) model without the intervention of a medical institution. Thereby, providing people with direct services for paternity testing, ancestry testing and disease susceptibility testing (DST) to infer diseases' predisposition. Genome analyses are partly motivated by curiosity and people often want to partake without fear of privacy invasion. Existing privacy protection solutions for DST adopt cryptographic techniques to protect the genome of a patient from the party responsible for computing the analysis. Said techniques include homomorphic encryption, which can be computationally expensive and could take minutes for only a few single-nucleotide polymorphisms (SNPs). A predominant approach is a solution that computes DST over encrypted data, but the design depends on a medical unit and exposes test results of patients to the medical unit, making the design uncomfortable for privacy-aware individuals. Hence it is pertinent to have an efficient privacy-preserving DST solution with a DTC service. We propose a novel DTC model that protects the privacy of SNPs and prevents leakage of test results to any other party save for the genome owner. Conversely, we protect the privacy of the algorithms or trade secrets used by the genome analyzing companies. Our work utilizes a secure obfuscation technique in computing DST, eliminating expensive computations over encrypted data. Our approach significantly outperforms existing state-of-the-art solutions in runtime and scales linearly for equivalent levels of security. As an example, computing DST for 10,000 SNPs requires approximately 96 milliseconds on commodity hardware. With this efficient and privacy-preserving solution which is also simulation-based secure, we open possibilities for performing genome analyses on collectively shared data resources.

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Blockchain technology allows running ordered transactions within an immutable public ledger in a decentralized manner. It consists of two processes: Advertisement where the participants become aware of the new transactions and chain extension where the validated transactions are added into a block by extending the chain. Existing blockchain protocols have mainly focused on the chain extension, though the advertisement process has not been dealt with in depth. More specifically, transaction advertisement is fulfilled in an altruistic manner and the same gossip-like propagation mechanism is used for both transactions and blocks. Consequently, the existing protocols are not only incompatible within a rational setting where participants try to maximize their benefit but also suffering from inefficient bandwidth usage. In this work, we propose Tulip, a two-layered blockchain framework, which enriches any blockchain protocol with respect to the aforementioned advertisement issues. Tulip treats the advertisement and chain extension processes separately. Transactions are collected and verified by a transaction collector, whereas extending the chain with a new block is done by a round leader. In order to achieve a generic framework, we model the basic functionalities of a ledger. These functionalities are realized by any existing blockchain protocol and responsible for the chain extension process. Thanks to the modular structure of Tulip, the chain extension process can be combined with an incentive compatible and bandwidth-efficient advertisement process. Tulip can be built upon any blockchain protocol while preserving the ledger properties persistence and liveness. Moreover, it enhances the liveness into strong liveness property which requires only one honest participant, rather than all, to advertise a transaction. In this manner, to the best of our knowledge, Tulip forms the first blockchain protocol having incentive compatible chain extension and transaction advertisement processes. Finally, we show that Tulip can be used to reduce redundant bandwidth usage by up to 99%.

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Processing encrypted data is a well-known solution when protecting privacy-sensitive data from untrusted processing units. However, data expansion, as a result of data encryption, makes undesired computational and communicational overheads in the cryptographic applications. Data packing is one of the useful tools to minimize the overheads. In this work, we introduce a novel approach for packing encrypted data based on the subset sum problem. We show that our data packing achieve high performance in reducing the overheads and it is significantly more efficient than existing techniques. Moreover, we show that our approach perfectly matches with secure searching protocols for secure data retrieval.

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The increasing demand for data mining in business intelligence has led to a significant growth in the adoption of data mining as a service paradigm which enables companies to outsource their data and mining tasks to a cloud service provider. Despite the popularity of the paradigm, the companies hesitate to enable the cloud providers' access to their data considering customer privacy and intellectual property. In this paper, we propose a privacy-preserving two-party protocol which aims to mine direct sequential patterns from outsourced protected data. We focus on direct sequential pattern mining since it is a widely used primitive in business process analysis. Considering the accuracy and confidentiality, we choose encryption over statistical methods for data protection and processing. To be able to process the encrypted data, we adopt a homomorphic encryption scheme, ElGamal cryptosystem. The novelty of our scheme is that it introduces an encryption switching method that enables us to use both multiplicative and additive homomorphism on ElGamal cryptosystem. The results of our analyses show that our protocol is more efficient than the state-of-the-art proposals in terms of computational cost with a similar communication cost.@en

Due to privacy threats associated with computation of outsourced data, processing data on the encrypted domain has become a viable alternative. Secure computation of encrypted data is relevant for analysing datasets in areas (such as genome processing, private data aggregation, cloud computations) that require basic arithmetic operations. Performing division operation over-all encrypted inputs has not been achieved using homomorphic schemes in non-interactive modes. In interactive protocols, the cost of obtaining an encrypted quotient (from encrypted values) is computationally expensive. To the best of our knowledge, existing homomorphic solutions on encrypted division are often relaxed to consider public or private divisor. We acknowledge that there are other techniques such as secret sharing and garbled circuits adopted to compute secure division, but we are interested in homomorphic solutions. We propose an efficient and interactive two-party protocol that computes the fixed-point quotient of two encrypted inputs, using an efficient and secure comparison protocol as a sub-protocol. Our proposal provides a computational advantage, with a linear complexity in the digit precision of the quotient. We provide proof of security in the universally composable framework and complexity analyses. We present experimental results for two cryptosystem implementations in order to compare performance. An efficient prototype of our protocol is implemented using additive homomorphic scheme (Paillier), whereas a non-efficient fully-homomorphic scheme (BGV) version is equally presented as a proof of concept and analyses of our proposal.@en

Protocols for securely testing the equality of two encrypted integers are common building blocks for a number of proposals in the literature that aim for privacy preservation. Being used repeatedly in many cryptographic protocols, designing efficient equality testing protocols is important in terms of computation and communication overhead. In this work, we consider a scenario with two parties where party A has two integers encrypted using an additively homomorphic scheme and party B has the decryption key. Party A would like to obtain an encrypted bit that shows whether the integers are equal or not but nothing more. We propose three secure equality testing protocols, which are more efficient in terms of communication, computation or both compared to the existing work. To support our claims, we present experimental results, which show that our protocols achieve up to 99% computation-wise improvement compared to the state-of-the-art protocols in a fair experimental set-up@en

Existing permissionless blockchain solutions rely on peer-to-peer propagation mechanisms, where nodes in a network transfer transaction they received to their neighbors. Unfortunately, there is no explicit incentive for such transaction propagation. Therefore, existing propagation mechanisms will not be sustainable in a fully decentralized blockchain with rational nodes. In this work, we formally define the problem of incentivizing nodes for transaction propagation. We propose an incentive mechanism where each node involved in the propagation of a transaction receives a share of the transaction fee. We also show that our proposal is Sybil-proof. Furthermore, we combine the incentive mechanism with smart routing to reduce the communication and storage costs at the same time. The proposed routing mechanism reduces the redundant transaction propagation from the size of the network to a factor of average shortest path length. The routing mechanism is built upon a specific type of consensus protocol where the round leader who creates the transaction block is known in advance. Note that our routing mechanism is a generic one and can be adopted independently from the incentive mechanism.@en

It is astonishing to see more and more services built on user-oriented data, providing numerous tools to improve ones daily life. Nowadays, data collected from numerous sources is being used to monitor daily activities, i.e., monitoring patients. These innovations allow for more cost-efficient and scalable solutions. Nevertheless, these types of services can pose a threat to the privacy of individuals due to the possibility of leaking highly privacy-sensitive data. Therefore, it is essential to design such systems in a privacy-preserving manner. Inspired by a real-life project in the health-care domain, we propose to secure the data using encryption, while enabling the involved parties to run queries directly on this encrypted data. A vital component of such a system is searching for specific data entries within a large dataset. In this work, we present two cryptographic protocols that complete such a query by creating an encrypted vector in a simulation secure way. These vectors cons ist of a 1 for intended database entry, whereas other items would be represented as a 0. By creating index tables before the execution of the queries, it has become possible to execute a search query with high performance. As we show in our analyses, it takes less than one second to find the matching encrypted data-entry within a database with 100K records. Our proposal is generic, can be applied to several application domains, and practically compared to similar works. @en

Many countries around the globe are investing on e-healthcare increasingly, which offers tremendous benefits to all stakeholders in healthcare. Nevertheless, this technology introduces unprecedented privacy concerns toward patients and raise more uncertainty among them to use e-healthcare for monitoring their vital signs. These concerns necessitate finding scientific solutions, which enable e-healthcare systems to process and analyze privacy-sensitive information, and offer services to the patients without violating their privacy. One of the approaches to address the privacy concerns is utilizing cryptographic techniques, which provide us tools to create Privacy-by-Design e-healthcare systems. Moreover, cryptographic solutions allow to process patients’ private information, while they are kept confidential and only known to the patients. Although using cryptographic technique is effective in providing privacy and processing private information, it results in high computational and communicational overhead. In fact, the current cryptographic building-blocks are not efficient enough for processing encrypted data in large-scale databases. In this paper, we address one of the highly used cryptographic building-blocks, which is checking the equality of two encrypted values. We investigate through the performance of the state-of-the-art secure equality tests and propose novel techniques to reduce their costs in terms of computation and communication. Then, through the complexity analysis and experimental results, we show 99% improvements in terms of computation is achieved. These improvements make the e-healthcare systems more attractive in terms of efficiency and in reach of practical applicability. @en

Genetic data are important dataset utilised in genetic epidemiology to investigate biologically coded information within the human genome. Enormous research has been delved into in recent years in order to fully sequence and understand the genome. Personalised medicine, patient response to treatments and relationships between specific genes and certain characteristics such as phenotypes and diseases, are positive impacts of studying the genome, just to mention a few. The sensitivity, longevity and non-modifiable nature of genetic data make it even more interesting, consequently, the security and privacy for the storage and processing of genomic data beg for attention. A common activity carried out by geneticists is the association analysis between allele-allele, or even a genetic locus and a disease. We demonstrate the use of cryptographic techniques such as homomorphic encryption schemes and multiparty computations, how such analysis can be carried out in a privacy friendly manner. We compute a 3 × 3 contingency table, and then, genome analyses algorithms such as linkage disequilibrium (LD) measures, all on the encrypted domain. Our computation guarantees privacy of the genome data under our security settings, and provides up to 98:4% improvement, compared to an existing solution.

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In this paper, we first discuss the essential requirements for a fingerprint (perceptual hash)-based distributed video identification system in peer-to-peer (P2P) networks in comparison with traditional central database implementations of fingerprints. This discussion reveals that first, fingerprint sizes of existing video fingerprint methods are not compatible with the cache sizes of current P2P clients; second, fingerprint extraction durations during a query are not at tolerable levels for a user in the network; third, the repetitive patterns in the extracted fingerprints avoid the uniform distribution of storage and traffic load among the peers; and finally, the existing methods do not provide a solution to synchronize the fingerprint extraction from the shared video and queried video. In order to solve the mentioned requirements, we propose a baseline method using only the difference of video frame means, which decreases the fingerprint sizes to typical cache sizes, by increasing the granularity levels from seconds to minutes. We then develop a novel algorithm which utilizes reference points on one-dimensional frame mean sequence for the synchronization of fingerprint extraction. This algorithm is extended with a hierarchical decoding approach based on Gaussian scales, which only decodes a subset of video frames without needing a full decoding. Finally, an analysis on the effect of design parameters to the fingerprint probability distribution is performed to avoid repetitive patterns. Our ultimate solution reduces the fingerprint sizes into kilobytes, extraction time to seconds, and search duration into milliseconds, and achieves about 90% detection rates with 1-4 min granularities, while enabling a fair distribution of storage load among the peers at the same time.@en

The growing complexity of software with respect to technological advances encourages model-based analysis of software systems for validation and verification. Process mining is one recently investigated technique for such analysis which enables the discovery of process models from event logs collected during software execution. However, the usage of logs in process mining can be harmful to the privacy of data owners. While for a software user the existence of sensitive information in logs can be a concern, for a software company, the intellectual property of their product and confidential company information within logs can pose a threat to company's privacy. In this paper, we propose a privacy-preserving protocol for the discovery of process models for software analysis that assures the privacy of users and companies. For this purpose, our proposal uses encrypted logs and processes them using cryptographic protocols in a two-party setting. Furthermore, our proposal applies data packing on the cryptographic protocols to optimize computations by reducing the number of repetitive operations. The experiments show that using data packing the performance of our protocol is promising for privacy-preserving software analysis. To the best of our knowledge, our protocol is the first of its kind for the software analysis which relies on processing of encrypted logs using process mining techniques.

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Secure equality testing of two private values is one of the fundamental building blocks of many cryptographic protocols designed for Signal Processing in the Encrypted Domain (SPED). Existing protocols introduce significant amount of computation and computational overhead, which makes it essential to search for new and novel, efficient equality tests for the design of SPED algorithms. In this paper, we first describe the state-of-The-Art equality tests, and then propose two cryptographic protocols which are significantly more efficient than the existing work. Our proposals achieve high performance due to algorithmic changes and successful deployment of data packing. Furthermore, we also present a novel secure exponentiation protocol as a part of our first equality test. Complexity and performance analyses clearly indicate the high efficiency of our protocols in terms of computation cost.

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In smart grids, providing power consumption statistics to the customers and generating recommendations for managing electrical devices are considered to be effective methods that can help to reduce energy consumption. Unfortunately, providing power consumption statistics and generating recommendations rely on highly privacy-sensitive smart meter consumption data. From the past experience, we see that it is essential to find scientific solutions that enable the utility providers to provide such services for their customers without damaging customers’ privacy. One effective approach relies on cryptography, where sensitive data is only given in the encrypted form to the utility provider and is processed under encryption without leaking content. The proposed solutions using this approach are very effective for privacy protection but very expensive in terms of computation and communication. In this paper, we focus on an essential operation for designing a privacy-preserving recommender system for smart grids, namely comparison, that takes two encrypted values and outputs which one is greater than the other one. We improve the state-of-the-art comparison protocol based on Homomorphic Encryption in terms of computation and communication by 56 and 25 %, respectively, by introducing algorithmic changes and data packing. As the smart meters are very limited devices, the overall improvement achieved is promising for the future deployment of such cryptographic protocols for enabling privacy enhanced services in smart grids.

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Validation in a big software system can be managed by analysis of its behaviour through occasionally collected event logs. Process mining is a technique to perform software validation by discovering process models from event logs or by checking the conformance of the logs to a process model. A well-known algorithm in process mining to discover process models is alpha algorithm. However, while utilising alpha algorithm is useful for software validation, the existence of some sensitive information in the log files may become a threat for the privacy of users. In this work, we propose a protocol for privacy-preserving alpha algorithm on encrypted data. Our protocol aims to generate process models for a software without leaking any information about its users. It achieves same computational complexity with the original algorithm despite the additional computation overhead.@en

The continuous decline in the cost of DNA sequencing has contributed bothpositive and negative feelings in the academia and research community. It hasnow become possible to harvest large amounts of genetic data, which researches believe their study will help improve preventive and personalised healthcare, better understanding of diseases and response to treatments. However, there are more information embedded in genes than are currently understood, just as a genomic data contains information of not just the owner, but relatives who might not subscribe to sharing them. Unrestricted access to genomic data can be privacy invasive, hence the urgent need to regulate access to them and develop protocols that would allow privacy-preserving techniques in both computations and analysis that involve these very sensitive data. In this work, we discuss how a careful combination of cryptographic primitives such as homomorphic encryption, can be used to privately implement common algorithms peculiar to genome-wide association studies (GWAS). This obviously comes at a cost, where we have to accommodate the trade-off between speed of computations and privacy.@en