In recent years, the widespread of mobile devices equipped with GPS and communication chips has led to the growing use of location-based services (LBS) in which a user receives a service based on his current location. The disclosure of user's location, however, can raise serious concerns about user privacy in general, and location privacy in particular which led to the development of various location privacy-preserving mechanisms aiming to enhance the location privacy while using LBS applications. In this paper, we propose to model the user mobility pattern and utility of the LBS as a Markov decision process (MDP), and inspired by probabilistic current state opacity notation, we introduce a new location privacy metric, namely $ε-$privacy, that quantifies the adversary belief over the user's current location. We exploit this dynamic model to design a LPPM that while it ensures the utility of service is being fully utilized, independent of the adversary prior knowledge about the user, it can guarantee a user-specified privacy level can be achieved for an infinite time horizon. The overall privacy-preserving framework, including the construction of the user mobility model as a MDP, and design of the proposed LPPM, are demonstrated and validated with real-world experimental data.
Tor and hidden services have emerged as a practical solution to protect user privacy against tracking and censorship. At the same time, little is known about the lifetime and nature of hidden services. Data collection and study of Tor hidden services is challenging due to its nature of providing privacy. Studying the lifetime of hidden services provides several benefits. For example, it allows investigation of the maliciousness of domains based on their lifetime. Short-lived hidden services are more likely not to be legitimate domains, e.g., used by ransomware, as compared to long-lived domain…
Many successful services rely on trustworthy contributions from users. To establish that trust, such services often require access to privacy-sensitive information from users, thus creating a conflict between privacy and trust. Although it is likely impractical to expect both absolute privacy and trustworthiness at the same time, we argue that the current state of things, where individual privacy is usually sacrificed at the altar of trustworthy services, can be improved with a pragmatic $Glimmer$ $of$ $Trust$, which allows services to validate user contributions in a trustworthy way without f…
Smart city is one of the most known Internet of Things (IoT) applications. The smart city services improve user's daily lives. However, security and privacy issues are slowing down their adoption. In addition, the characteristics of IoT devices, applications and users make security implementation of the considered applications a challenging task. To address these issues, we present, in this paper, a new context-aware security and privacy architecture for the IoT. Thanks to the "as a service" approach, this new architecture will be user-centric. It will also support known context-aware security…
Global corporations (e.g., Google and Microsoft) have recently introduced a new model of cloud services, fuzzing-as-a-service (FaaS). Despite effectively alleviating the cost of fuzzing, the model comes with privacy concerns. For example, the end user has to trust both cloud and service providers who have access to the application to be fuzzed. Such concerns are due to the platform is under the control of its provider and the application and the fuzzer are highly coupled. In this paper, we propose P2FaaS, a new ecosystem that preserves end user's privacy while providing FaaS in the cloud. The …
A privacy-preserving Context-Aware Publish-Subscribe System (CA-PSS) enables an intermediary (broker) to match the content from a publisher and the subscription by a subscriber based on the current context while preserving confidentiality of the subscriptions and notifications. While a privacy-preserving Ride-Hailing Service (RHS) enables an intermediary (service provider) to match a ride request with a taxi driver in a privacy-friendly manner. In this work, we attack a privacy-preserving CA-PSS proposed by Nabeel et al. (2013), where we show that any entity in the system including the broker …
The recent SARS-CoV-2 pandemic gave rise to management approaches using mobile apps for contact tracing. The corresponding apps track individuals and their interactions, to facilitate alerting users of potential infections well before they become infectious themselves. Naive implementation obviously jeopardizes the privacy of health conditions, location, activities, and social interaction of its users. A number of protocol designs for colocation tracking have already been developed, most of which claim to function in a privacy preserving manner. However, despite claims such as "GDPR compliance…
In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users may want to protect their privacy by minimizing the amount of information they disclose to curious service providers. Service providers are interested in verifying the integrity of the users' data to improve their services and obtain useful knowledge for their business. In this work, we present a generic solution to the trade-off between privacy, integrity, …
Location-based services are getting more popular day by day. Finding nearby stores, proximity-based marketing, on-road service assistance, etc., are some of the services that use location-based services. In location-based services, user information like user identity, user query, and location must be protected. Ma et al. (INFOCOM-BigSecurity 2019) proposed a privacy-preserving location-based service using Somewhat Homomorphic Encryption (SHE). Their protocol uses edge nodes that compute on SHE encrypted location data and determines the $k$-nearest points of interest contained in the Location-b…
With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in…
Single sign-on authentication systems such as OAuth 2.0 are widely used in web services. They allow users to use accounts registered with major identity providers such as Google and Facebook to login on multiple services (relying parties). These services can both identify users and access a subset of the user's data stored with the provider. We empirically investigate the end-user privacy implications of OAuth 2.0 implementations in relying parties most visited around the world. We collect data on the use of OAuth-based logins in the Alexa Top 500 sites per country for five countries. We categ…
Major cloud operators offer machine learning (ML) as a service, enabling customers who have the data but not ML expertise or infrastructure to train predictive models on this data. Existing ML-as-a-service platforms require users to reveal all training data to the service operator. We design, implement, and evaluate Chiron, a system for privacy-preserving machine learning as a service. First, Chiron conceals the training data from the service operator. Second, in keeping with how many existing ML-as-a-service platforms work, Chiron reveals neither the training algorithm nor the model structure…
Location-based Services (LBSs) provide valuable services, with convenient features for users. However, the information disclosed through each request harms user privacy. This is a concern particularly with honest-but-curious LBS servers, which could, by collecting requests, track users and infer additional sensitive user data. This is the motivation of both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome the disadvantages of…
Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially because LBS providers can be honest-but-curious, collecting queries and tracking users' whereabouts and infer sensitive user data. This motivated both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes ove…
With the prevalence of location-based services (LBSs) supported by advanced positioning technology, there is a dramatic increase in the transmission of high-precision personal geographical data. Malicious use of these sensitive data will threaten the privacy of LBS users. Although privacy research in LBSs has received wide attention, related works are mostly focused on some specific applications. Due to high diversity of LBSs, it is critical to build a universal model that is able to handle privacy protection for broader range of applications. In this paper, we propose a Computationally Recove…
Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy concerns by collecting sensitive locations. A strong privacy model ''local differential privacy'' (LDP) has been recently deployed in many different applications (e.g., Google RAPPOR, iOS, and Microsoft Telemetry) but not effective for LBS applications due to the low utility of existing LDP mechanisms. To address such deficiency, we propose the first LDP framework for a variety of location-based services (namely ''L-SRR'…
In a typical ride-hailing service, the service provider (RS) matches a customer (RC) with the closest vehicle (RV) registered to this service. TRACE is an efficient privacy-preserving ride-hailing service proposed by Wang et al. in 2018. TRACE uses masking along with other cryptographic techniques to ensure efficient and accurate ride-matching. The RS uses masked location information to match RCs and RVs within a quadrant without obtaining their exact locations, thus ensuring privacy. In this work, we disprove the privacy claims in TRACE by showing the following: a) RCs and RVs can identify th…
Location-based queries enable fundamental services for mobile road network travelers. While the benefits of location-based services (LBS) are numerous, exposure of mobile travelers' location information to untrusted LBS providers may lead to privacy breaches. In this paper, we propose StarCloak, a utility-aware and attack-resilient approach to building a privacy-preserving query system for mobile users traveling on road networks. StarCloak has several desirable properties. First, StarCloak supports user-defined k-user anonymity and l-segment indistinguishability, along with user-specified spat…
The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users' privacy. Existing privacy preserving protocols require trusted nodes, with serious security and computational bottlenecks. In this paper, we propose a novel distributed anonymizing protocol based on peer-to-peer architecture. Each mobile node is responsible for anonymizing a specific zone. The mobile nodes collaborate in anonymizing their queries, without the need not get access to any information about each other. In the proposed protocol, each request will b…
Aiming at the privacy preservation of dynamic Web service composition, this paper proposes a SDN-based runtime security enforcement approach for privacy preservation of dynamic Web service composition. The main idea of this approach is that the owner of service composition leverages the security policy model (SPM) to define the access control relationships that service composition must comply with in the application plane, then SPM model is transformed into the low-level security policy model (RSPM) containing the information of SDN data plane, and RSPM model is uploaded into the SDN controlle…
In order for VASPs to fulfill the regulatory requirements from the FATF and the Travel Rule, VASPs need access to truthful information regarding originators, beneficiaries and other VASPs involved in a virtual asset transfer instance. Additionally, in seeking data regarding subjects (individuals or organizations) VASPs are faced with privacy regulations such as the GDPR and CCPA. In this paper we a propose privacy-preserving claims issuance model that carries indicators of the provenance of the data and the algorithms used to derive the claim or assertion. This allows VASPs to obtain originato…
Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue we introduce a model of an adversary with imperfect knowledge about…
The massive upsurge in computational and storage has driven the local data and machine learning applications to the cloud environment. The owners may not fully trust the cloud environment as it is managed by third parties. However, maintaining privacy while sharing data and the classifier with several stakeholders is a critical challenge. This paper proposes a novel model based on differential privacy and machine learning approaches that enable multiple owners to share their data for utilization and the classifier to render classification services for users in the cloud environment. To process…
Automatic service discovery is essential to realizing the full potential of the Internet of Things (IoT). While discovery protocols like Multicast DNS, Apple AirDrop, and Bluetooth Low Energy have gained widespread adoption across both IoT and mobile devices, most of these protocols do not offer any form of privacy control for the service, and often leak sensitive information such as service type, device hostname, device owner's identity, and more in the clear. To address the need for better privacy in both the IoT and the mobile landscape, we develop two protocols for private service discover…
This paper proposes a privacy protection and evaluation method for location services based on edge computing environment. By constructing the site service data protection and system evaluation system in the edge computing environment, based on the existing user privacy protection work, the data processing module and service evaluation module are constructed, and the evaluation algorithm is designed. NPE evaluation model and POE evaluation model are designed according to relevant research recommended by IPE. Specifically, in the NPE evaluation model, we regard each user's decision as a group of…
Defending against distributed denial of service (DDoS) attacks in the Internet is a fundamental problem. However, recent industrial interviews with over 100 security experts from more than ten industry segments indicate that DDoS problems have not been fully addressed. The reasons are twofold. On one hand, many academic proposals that are provably secure witness little real-world deployment. On the other hand, the operation model for existing DDoS-prevention service providers (e.g., Cloudflare, Akamai) is privacy invasive for large organizations (e.g., government). In this paper, we present Um…
Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or disclosure remains a key concern during the sharing service development process. While significant efforts have been undertaken to address various privacy issues in recent years, there is a surprising lack of review for privacy concerns in the cyber-enabled sharing world. To bridge the gap, in this study, we survey and evaluate existing and emerging privacy issues relating to sh…
Location-Based Recommendation Services (LBRS) has seen an unprecedented rise in its usage in recent years. LBRS facilitates a user by recommending services based on his location and past preferences. However, leveraging such services comes at a cost of compromising one's sensitive information like their shopping preferences, lodging places, food habits, recently visited places, etc. to the third-party servers. Losing such information could be crucial and threatens one's privacy. Nowadays, the privacy-aware society seeks solutions that can provide such services, with minimized risks. Recently, …
Confidentiality without Encryption For Cloud Computational Privacy
技术的进步已经产生了新的计算模型，任何个人/组织云服务消费者（这里表示为CSC）可以将其数据上的计算密集型任务外包给远程云服务提供商（CSP），以获得许多优势，例如更低的成本，可扩展性等，但这些优势带来了更大的成本“数据的安全和隐私”正是由于这个原因，许多CSC对转向云计算模型持怀疑态度。虽然密码学研究的进展是有希望的，但还没有实际的解决方案来对加密数据执行任何操作[1]。正是由于这个原因，我们迫切需要找到其他可行的解决方案，以使我们从云计算中受益。过去提出了一种无需加密就能提供机密性的技术，即Ronald L. Rivest [2].虽然这种技术已被提出用于基于分组的通信系统，但它并不适用于所有云服务模型，如软件即服务，平台即服务或云结构即服务[3]。在本文中，我们提出了一种适应这种技术在云计算设置CSC的外包计算密集型任务，如Web日志解析，DNA测序等MapReduce像CSP服务。
In recent years, there have been many cloud-based machine learning services, where well-trained models are provided to users on a pay-per-query scheme through a prediction API. The emergence of these services motivates this work, where we will develop a general notion of model privacy named imitation privacy. We show the broad applicability of imitation privacy in classical query-response MLaaS scenarios and new multi-organizational learning scenarios. We also exemplify the fundamental difference between imitation privacy and the usual data-level privacy.
Privacy is a key challenge for continued digitalization of health. The forthcoming European General Data Protection Regulation (GDPR) is transforming this challenge into regulatory directives. User consent provisioning and coordinating across data services will be the keys in addressing this challenge. We suggest a privacy-driven architecture that provides tools for providing user consent as a service. This enables managing and reusing private health information between a large amount of data sources, individuals and services, even when they are not known beforehand. The proposed architecture …
Cryptocurrencies typically aim at preserving the privacy of their users. Different cryptocurrencies preserve privacy at various levels, some of them requiring users to rely on strategies to raise the privacy level to their needs. Among those strategies, we focus on two of them: merge avoidance and mixing services. Such strategies may be adopted on top of virtually any blockchain-based cryptocurrency. In this paper, we show that whereas optimal merge avoidance leads to an NP-hard optimization problem, incentive-compatible mixing services are subject to a certain class of impossibility results. …
The online privacy and security of the disabled community is a complex field that has implications for every user who navigates web services. While many disciplines have separately researched the disabled population and their online privacy and security concerns, the overlap between the two is very high but under-researched. Moreover, a complex relationship exists between the disabled population and web services where the interaction depends on several web service developmental factors, including usability and accessibility. To this aid, we explored this intersection of privacy and security of…
The management of identities on the Internet has evolved from the traditional approach (where each service provider stores and manages identities) to a federated identity management system (where the identity management is delegated to a set of identity providers). On the one hand, federated identity ensures usability and provides economic benefits to service providers. On the other hand, it poses serious privacy threats to users as well as service providers. The current technology, which is prevalently deployed on the Internet, allows identity providers to track the user's behavior across a b…
