skip to main content


Title: Ancile: Enhancing Privacy for Ubiquitous Computing with Use-Based Privacy
Widespread deployment of Intelligent Infrastructure and the In- ternet of Things creates vast troves of passively-generated data. These data enable new ubiquitous computing applications—such as location-based services—while posing new privacy threats. In this work, we identify challenges that arise in applying use-based privacy to passively-generated data, and we develop Ancile, a plat- form that enforces use-based privacy for applications that consume this data. We find that Ancile constitutes a functional, performant platform for deploying privacy-enhancing ubiquitous computing applications.  more » « less
Award ID(s):
1642120 1700832
NSF-PAR ID:
10134021
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Workshop on Privacy in the Electronic Soceity
Page Range / eLocation ID:
111 to 124
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Summary

    The ubiquitous use of location‐based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long‐term observation of privacy‐preserving queries. We show that existing privacy‐preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy‐preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimumk‐locations,l‐diversity, and privacyareapreservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long‐term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real‐world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state‐of‐the‐art methods.

     
    more » « less
  2. The vehicular fog is a relatively new computing paradigm where fog computing works with the vehicular network. It provides computation, storage, and location-aware services with low latency to the vehicles in close proximity. A vehicular fog network can be formed on-the-fly by adding underutilized or unused resources of nearby parked or moving vehicles. Interested vehicles can outsource their resources or data by being added to the vehicular fog network while maintaining proper security and privacy. Client vehicles can use these resources or services for performing computation-intensive tasks, storing data, or getting crowdsource reports through the proper secure and privacy-preserving communication channel. As most vehicular network applications are latency and location sensitive, fog is more suitable than the cloud because of the capability of performing calculations with low latency, location awareness, and the support of mobility. Architecture, security, and privacy models of vehicular fog are not well defined and widely accepted yet as it is in its early stage. In this paper, we have analyzed existing studies on vehicular fog to determine the requirements and issues related to the architecture, security, and privacy of vehicular fog computing. We have also identified and highlighted the open research problems in this promising area. 
    more » « less
  3. Abstract Background

    Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.

    Results

    Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.

    Conclusions

    Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.

    Short Abstract

    Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

     
    more » « less
  4. The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead. 
    more » « less
  5. null (Ed.)
    Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en D -to-end mod e l m a rketp l ace with diff e rential p r ivacy ( Dealer ) towards answering the following questions: How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of Dealer and verify the efficiency and effectiveness of the proposed algorithms. 
    more » « less