Using Granule to Search Privacy Preserving Voice in Home IoT Systems

Abstract

The Home IoT Voice System (HIVS) such as Amazon Alexa or Apple Siri can provide voicebased interfaces for people to conduct the search tasks using their voice. However, how to protect privacy is a big challenge. This paper proposes a novel personalized search scheme of encrypting voice with privacypreserving by the granule computing technique. Firstly, Mel-Frequency Cepstrum Coefficients (MFCC) are used to extract voice features. These features are obfuscated by obfuscation function to protect them from being disclosed the server. Secondly, a series of definitions are presented, including fuzzy granule, fuzzy granule vector, ciphertext granule, operators and metrics. Thirdly, the AES method is used to encrypt voices. A scheme of searchable encrypted voice is designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granule of the voice. The experiments are conducted on corpus including English, Chinese and Arabic. The results show the feasibility and good performance of the proposed scheme.

On the Security and Privacy Challenges of Virtual Assistants

“…The study of the selected papers led us to deduce the following main findings: The processes that check third-party extensions submitted to the app stores of both Amazon and Google do a demonstrably poor job of ensuring that the apps properly authenticate from the third-party server to the Alexa/Google cloud. Several novel methods of user authentication to the VA device have been proposed, each using a different secondary device to offer a form of two-factor authentication [ 16 , 23 , 31 ]. Each of the user authentication methods do go some way to mitigating the voice/replay attacks outlined in the findings of RQ1.…”

“…Several novel methods of user authentication to the VA device have been proposed, each using a different secondary device to offer a form of two-factor authentication [ 16 , 23 , 31 ].…”

“…Moving away from extension application development, three novel approaches that might suggest a better way in which VA companies might improve security for end-users have been proposed. Feng et al [ 23 ] presented what they call ‘VAuth’, a method of ‘continuous’ authentication, in which a wearable device collects unique body surface vibrations emanating from the user and matches them with the voice signal heard by the VA. Wang et al [ 31 ] proposed another wearable that might provide two-factor authentication. In this approach, termed ‘WearID’, however, the wearable in this instance captures unique vibration patterns not from the user’s body but from the vibration domain of the user’s voice.…”

Fuzzy Granule Manifold Alignment Preserving Local Topology

“…In addition, Li et al presented a fastest robust path optimization algorithm to form the best routing by combining the effect of traffic events [52]. Li and his colleagues adopted granular computing to design boosted k nearest neighbor classifiers [53] and search voice encrypted scheme [54]. Mencar and Pedrycz proposed the definition of granular counting, which realized in the presence of uncertain data modeled through possibility distributions [55] and this is also a new angle to study uncertain data.…”

Fuzzy Granular Hyperplane Classifiers

“…There are a lot of applications of Granular Computing. And these applications like forecasting time series [40], [41], manufacturing [42], search encryption voice [43], prediction tasks [44], concept learning [45], optimization [46], K-nearest granule classifiers [47], attribute reduction [48], analysis of microarray data [49] and so on, are reported in recent studies. It is of worthy of emphasizing that information granules infiltrate almost all researches.…”