On Privacy Leakage through Silence Suppression
نویسنده
چکیده
Silence suppression, an essential feature of speech communications over the Internet, saves bandwidth by disabling voice packet transmission when silence is detected. On the other hand, silence suppression enables an adversary to recover talk patterns from packet timing. In this paper, we investigate privacy leakage through the silence suppression feature. More specifically, we propose a new class of traffic analysis attacks to encrypted speech communication with the goal of detecting speakers of encrypted speech communications. We evaluate the proposed attacks by extensive experiments over different type of networks including commercialized anonymity networks and campus networks. The experiments show that the proposed traffic analysis attacks can detect speakers of encrypted speech communications with high
منابع مشابه
Analyzing Tools and Algorithms for Privacy Protection and Data Security in Social Networks
The purpose of this research, is to study factors influencing privacy concerns about data security and protection on social network sites and its’ influence on self-disclosure. 100 articles about privacy protection, data security, information disclosure and Information leakage on social networks were studied. Models and algorithms types and their repetition in articles have been distinguished a...
متن کاملOptimal Utility-Privacy Trade-off with the Total Variation Distance as the Privacy Measure
Three reasons are provided in favour of L-norm as a measure of privacy-leakage: i) It is proved that this measure satisfies post-processing and linkage inequalities that make it consistent with an intuitive notion of a privacy measure; ii) It is shown that the optimal utility-privacy trade-off can be efficiently solved through a standard linear program when Lnorm is employed as the privacy meas...
متن کاملLOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
Recent advances in machine learning are paving the way for the artificial generation of high quality images and videos. In this paper, we investigate how generating synthetic samples through generative models can lead to information leakage, and, consequently, to privacy breaches affecting individuals’ privacy that contribute their personal or sensitive data to train these models. In order to q...
متن کاملPrivacy Leakage via De-anonymization and Aggregation in Heterogeneous Social Networks
Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS firstly leverages the network graph structure to s...
متن کاملMeasurement of Privacy Leakage Tolerance on the Mobile Internet
On the mobile Internet, individual identification and information transmission are more convenient. With the popularity of mobile Internet, private information of users is easily accessed. Appropriate access to users’ information can help enterprises understand consumers’ demand better and improve marketing effectiveness. However, grabbing users’ information excessively may increase the costs o...
متن کامل