Privacy-Preserving Location-Based Services by using Intel SGX

Kulkarni, Vaibhav; Chapuis, Bertil; Garbinato, Benoît

doi:10.1145/3144730.3144739

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…Protection for flow tracking application [59] Trusted framework to develop IoT applications [36] Protection for health data [73] Secure architecture for P2P scenarios [24] Protection for edge computing [63] Comparison between TEE and secure multi-party computation application [62] Protection for video application [46], [69] Secure logger [13], [76] Protection for system analyser [49] Societal model for IoT security [68] Protection for location-based services [60] Trusted auditor [47] Protection for data dissemination [61] Data encryption mechanism [56] Protection for data management [14] Data protection (app) [71] Protection for data aggregation (app) [75] Checker for Industrial gateway communications [52] Lightweithg anonymous authentication [25] Remote attestation mechanism [38] Device snapshot authentication system [32] Control-flow attestation [37] Authentication scheme [58] Remote attestation and channel protection [44] Secure authentication and key distribution [67] Boot attestation [53] Protection for data through authentication [50] Protection and attestation for remote terminal [65] Device private keys protection architecture [28] Remote attestation [64] Keys derivation from device characteristics [31] Authenticity detection service [40] Keys protection against cold boot attacks [55] Cache rootkit exploiting TrustZone [35] vendors, e.g., ARM and Intel, already present many of the general advantages, such as hardware isolation (normal world and the secure world) and mem...…”

Section: Tee Advantages and Disadvantagesmentioning

confidence: 99%

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Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

et al. 2021

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Trusted Execution Environments have been applied to improve data security in many distinct application scenarios since they enable data processing in a separate and protected region of memory. To investigate how this technology has been applied to the different IoT scenarios, which commonly deal with specific characteristics such as device resource constraints, we carried out a systematic literature review. For this, we selected and analyzed 58 papers from different conferences and journals, identifying the main IoT solutions and scenarios in which TEE has been employed. We also gathered the mentioned TEE advantages and disadvantages as well as the suggestions for future works. This study gives a general overview of the use of TEEs for cloud/fog-based IoT applications, bringing some challenges and directions.

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Section: Tee Advantages and Disadvantagesmentioning

confidence: 99%

“…Qin et al [52] recommended adding security enforcement policy for their industrial gateway communications checker. Kulkarni et al [60] proposed to evaluate their solution, a protection system for location-based services, and compare it with other approaches.…”

Section: Suggested Future Workmentioning

confidence: 99%

Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

et al. 2021

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“…The data is encrypted using Paillier cryptosystem [82] and then put into statistical tests within a secure enclave. Besides these works, more SGX-based schemes are popular in cloud computing and applied in practical fields, like healthcare [83], machine learning [84], [85], data analysis [86], location-based services [87], and many others.…”

Section: Software Guard Extensionsmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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“…Here, we consider three parameters for evaluation: (1) Max dist , (2) Min time , and (3) Min visit . We do not take service utility in to account as we assume a system based on a trusted computing environment, which does not compromise on service utility [20]. However, in case of techniques such as obfuscations or anonymization, pseudo-locations are used for the last hop, in which case the accuracy depends on the amount of distortion added to the user's true location.…”

Section: Privacy Analysismentioning

confidence: 99%

“…This model reverses the client/server relationship by enforcing the service providers to query for the required data from the model present on the user smartphone. Finally, the query response can be processed in a trusted computing environment as illustrated in our previous work [20]. Judicious scheduling in such systems ensures that learning occurs only when the device is completely idle [23].…”

Section: Introductionmentioning

confidence: 99%

Capstone: Mobility Modeling on Smartphones to Achieve Privacy by Design

Kulkarni

Moro

Chapuis

et al. 2018

2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/ 12th IEEE International

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Sharing location traces with context-aware service providers has privacy implications. Location-privacy preserving mechanisms, such as obfuscation, anonymization and cryptographic primitives, have been shown to have impractical utility/privacy tradeoff. Another solution for enhancing user privacy is to minimize data sharing by executing the tasks conventionally carried out at the service providers' end on the users' smartphones. Although the data volume shared with the untrusted entities is significantly reduced, executing computationally demanding server-side tasks on resourceconstrained smartphones is often impracticable. To this end, we propose a novel perspective on lowering the computational complexity by treating spatiotemporal trajectories as space-time signals. Lowering the data dimensionality facilitates offloading the computational tasks onto the digital-signal processors and the usage of the non-blocking signal-processing pipelines. While focusing on the task of user mobility modeling, we achieve the following results in comparison to the state of the art techniques: (i) mobility models with precision and recall greater than 80%, (ii) reduction in computational complexity by a factor of 2.5, and (iii) reduction in power consumption by a factor of 0.5. Furthermore, our technique does not rely on users' behavioral parameters that usually result in privacy-leakage and conclusive bias in the existing techniques. Using three real-world mobility datasets, we demonstrate that our technique addresses these weaknesses while formulating accurate user mobility models.Problem 1: Mobility Signal Generation. Given a trajectory T u of an individual u, a temporally ordered sequence of tuples, 3 Art. 23: www.privacy-regulation.eu/en/article-23-restrictions-GDPR.htm such that, T u = (l 1 ,t 1 ), (l 2 ,t 2 )...(l n ,t n ) , where l i = (lat i , lon i ), the latitude-longitude coordinate pair and t, the timestamp such that t i+1 > t i , translate T u into a 2-D signal S u (t), modeled as a function of changing distance with respect to time.Requirements and Challenges. (i) Constructing a continuous graph from the noisy and non-uniformly sampled location trajectories, (ii) preserving all the key knowledge contained in the trajectory samples, and (iii) retaining the spatial locality between the discretized points.Problem 2: Signal Interpretation. Given a user's spatiotemporal signal S u (t), interpret and model the distinct signal elements in the temporal and spectral domain, i.e. local maxima/minima, rising/falling edges, static signal component, candidate frequencies, spectral coefficients and harmonics, with respect to human mobility behaviors.Requirement and Challenge. In order to facilitate interdomain switching, attach and validate a semantic meaning to each of the above signal components.Problem 3: Mobility Modeling. Given the signal S u (t) and the valid interpretation of each element, construct the user's mobility model in terms of a graph G u (ROI, Tr), where ROI = {ROI 1 , ROI 2 ...ROI n } is the set of all the re...

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Privacy-Preserving Location-Based Services by using Intel SGX

Cited by 8 publications

References 14 publications

Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

Capstone: Mobility Modeling on Smartphones to Achieve Privacy by Design

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