Who Moved My Data? Privacy Protection in Smartphones

Dai, Wei; Qiu, Meikang; Qiu, Longfei; Chen, Longbin; Wu, Ana

doi:10.1109/mcom.2017.1600349cm

Cited by 129 publications

(26 citation statements)

References 12 publications

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“…Certos tipos de permissões trazem riscos à privacidade, pois alguns aplicativos capturam mais do que seria necessário para executar suas funções. Captura abusiva de dados são mais difíceis de serem detectados do que um vírus (Dai et al, 2017). Tabela 3.…”

Section: Permissõesunclassified

Utilização e compartilhamento de dados capturados por aplicativos de smartphones: uma revisão sistemática de literatura

Silva¹,

Rosa²,

Ferneda³

et al. 2021

RGCTI

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As pesquisas sobre segurança de dados têm avançado nos últimos anos influenciadas pela expansão de aparelhos móveis, cada vez mais presentes na vida das pessoas. No mesmo sentido, também cresce a importância dada por nossa socie-dade à privacidade e segurança dos dados gerados ou armazenados nesses dispositivos. Assim, pesquisadores se veem estimu-lados a compreender de que forma esses dados podem ser acessados por aplicativos e pelo próprio sistema operacional neles instalados. Esse é um tema de pesquisa relativamente novo, mas já com significativa quantidade de trabalhos publicados na última década. Dessa forma, é pertinente uma sistematização de conhecimento para um melhor direcionamento de pesquisas sobre o assunto. O objetivo deste trabalho é realizar uma revisão sistemática de literatura sobre a utilização de informações produzidas em smartphones.

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Section: Permissõesunclassified

Utilização e compartilhamento de dados capturados por aplicativos de smartphones: uma revisão sistemática de literatura

Silva¹,

Rosa²,

Ferneda³

et al. 2021

RGCTI

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“…In the proposed security scheme based on key disturbance, the base station shares a key with each sensor node in the proposed scheme. The data uploaded is composed of the original data and the key of the node, and the privacy protection of the original data and the aggregated data is realized [7]. In the data aggregation privacy protection protocol proposed by Arora et al, the base station and other nodes are unable to obtain the privacy data of a single node [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

Guo

Zhang

2018

Int. J. Onl. Eng.

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<p class="0abstract"><span lang="EN-US">To improve the data aggregation privacy protection scheme in wireless sensor network (WSN), a new scheme is put forward based on the privacy protection of polynomial regression and the privacy protection method based on the homomorphic encryption. The polynomial data aggregation (PRDA+) protocol is also proposed. In this scheme, the node and the base station will pre-deploy a secret key, and the random number generator encrypts the random number for the seed through the private key, which protects the privacy of the data. Then, by comparing the decrypted aggregate data through the correlation between the two metadata, the integrity protection of the data is realized. A weighted average aggregation scheme that can be verified is proposed. In view of the different importance of user information, the corresponding weights are set for each sensor node. EL Gamal digital signature is used to authenticate sensor nodes. The results show that the signature verification algorithm enables the scheme to resist data tampering and data denial, and to trace the source of erroneous data.</span></p>

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“…an advanced persistent threat (APT), refers to a type of threat in which threat actors actively pursue and compromise the entity infrastructure of a target while maintaining anonymity [1]- [3]. APT attacks are of particular concern for organizations and individuals whose information is at risk of a data breach [4]. Among an assortment of ways to deliver APT attacks, the act of sending an email from an account purporting to be a trusted sender to specific and wellresearched targets is known as a targeted email attack or spear phishing.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Techniques to Identify Deceptive Decoy Documents Associated With Targeted Email Attacks

et al. 2021

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Detecting and preventing targeted email attacks is a long-standing challenge in cybersecurity research and practice. A typical targeted email attack capitalizes on a sophisticated email message to persuade a victim to run a specific, seemingly innocuous, action such as opening a link or an attachment and downloading and installing a software program. To successfully perform such an attack without being noticed afterwards, the attached exploit documents (hereafter referred to as decoy documents), must contain content that is highly relevant to the target. An analysis of such decoy documents can provide crucial information for inferring and identifying the targeted or potentially harmed victims. In this paper, we propose an automatic approach that leverages natural language processing and machine learning to identify decoy documents that have a high chance of deceiving the targeted users. The experimental results show that the proposed method provides good prediction accuracy: the best result obtained on a collection of 200 Chinese decoy documents yielded an accuracy of 97.5%, an F-measure of 97.9% and a low FPR of 3.1%. The proposed scheme can be deployed at various access points to fortify the defense against targeted email attacks that threaten various targets.INDEX TERMS Targeted email attack, decoy document, machine learning, natural language processing

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Who Moved My Data? Privacy Protection in Smartphones

Cited by 129 publications

References 12 publications

Utilização e compartilhamento de dados capturados por aplicativos de smartphones: uma revisão sistemática de literatura

Utilização e compartilhamento de dados capturados por aplicativos de smartphones: uma revisão sistemática de literatura

Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

Leveraging Machine Learning Techniques to Identify Deceptive Decoy Documents Associated With Targeted Email Attacks

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