Privacy protection control for mobile apps users

Cerf, Sophie; Robu, Bogdan; Marchand, Nicolas; Bouchenak, Sara

doi:10.1016/j.conengprac.2023.105456

Cited by 3 publications

(3 citation statements)

References 49 publications

(69 reference statements)

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“…Proposition 2: Given l * N as in (10) and denoting by S the solution set of ( 9), the following claim holds: i) If l N = l * N , i.e, if the actual location at time t is equal to the centroid of the previous N − 1 transmitted locations, then the solution set of ( 9) is…”

Section: B Optimal Obfuscated Locationmentioning

confidence: 99%

“…This work tackles the privacy protection challenge with a dynamic system's perspective. Privacy control with a reactive PI controller [10] allows for protection with negligible computing overhead; however, with limited precision and slow reaction. Model Predictive Control significantly improves privacy performances [11], but requires the knowledge of future locations and has a high computing cost.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

React to the Worst: Lightweight and Proactive Protection of Location Privacy

Molina

Fiacchini

Cerf

et al. 2023

IEEE Control Syst. Lett.

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This work presents a novel optimal control method for privacy protection of mobility data. Protection is based on data obfuscation, consisting in sending to the geolocated service a finely tuned fake location. The objective is twofold, keeping privacy values at an acceptable level and guaranteeing a reasonable utility loss, with a lightweight algorithm able to run on mobile devices. The proposed method consists of an offline modeling stage, based on privacy worst-case anticipation, and a fast algorithm executed online. In the offline stage, the algorithm computes the average amount of allowed utility loss necessary to maintain the privacy value of the following h steps above a given lower bound. For this purpose, the worst possible scenario over the future steps is computed and compared with the privacy function of the solution obtained by an MPC method. The online stage uses the information computed offline to solve an optimization problem whose decision variable is the location to transmit and whose objective is to maintain the privacy value above a minimal level, by avoiding large utility losses. The method is validated on an instance of a database of real records and compared with a state-of-the-art competitor.

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Section: B Optimal Obfuscated Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

React to the Worst: Lightweight and Proactive Protection of Location Privacy

Molina

Fiacchini

Cerf

et al. 2023

IEEE Control Syst. Lett.

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“…In the process of campus informatization, information is silently spreading to all aspects of students' learning and life, and everyone is active in the information campus as information data [9][10]. At the same time, students who rely on information data in performing any activity put a large amount of their information data in the information campus [11][12]. There is not only subtle chatting information and social software information in study and life, but even students' personal identity information, personal property information, personal health information and other personal privacy information [13][14].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Smart Campus Cybersecurity and Student Privacy Protection Paths Based on Markov Models

Jianhua

2023

Applied Mathematics and Nonlinear Sciences

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This paper starts with the application of hyper-convergence technology, builds the framework of a university smart campus based on it, and gives the framework description of the smart campus. In order to analyze the network security for the smart campus, the Markov model is used as the basis combined with the reinforced Q learning algorithm for network node security detection, and a specific simulation analysis is given. The encryption performance and defense performance of the elliptic curve cryptosystem are analyzed through the elliptic curve cryptosystem to formulate the encryption scheme for students’ private data in the smart campus. The results indicate that the Markov model node detection combined with reinforcement Q-learning in this paper takes a maximum time of about 5.75s when the network node size reaches 150. When the number of nodes in the smart campus network is 30, under brute force attack, the whole network is captured only when the number of malicious nodes increases to more than 22, while under random attack, it takes as many as 30 malicious nodes to join before the network completely falls. This illustrates that the use of the Markov model can be realized to analyze the network security of the smart campus and that student privacy protection needs to further improve the awareness of student data privacy protection and develop the habit of assessing the privacy risk beforehand in their daily network behavior.

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