Search for: All records

This paper examines an existential threat to Tor— the increasing frequency at which websites apply discriminatory behavior to users who arrive via the anonymity network. Our main contribution is the introduction of Tor exit bridges. Exit bridges, constructed as short-lived virtual machines on cloud service providers, serve as alternative egress points for Tor and are designed to bypass server-side censorship. Due to the proliferation of managed cloud-based desktop services (e.g., Amazon Workspaces), there is already a surprisingly large fraction of web requests that originate in the cloud. Trivially disrupting exit bridges by blocking requests from the cloud would thus lead to significant collateral damage. Our experiments demonstrate that exit bridges effectively circumvent server-side blocking of Tor with low overhead. Ad- ditionally, we perform a cost-analysis of exit bridges and show that even a large-scale deployment can be done at low cost. 

Encrypted voice-over-IP (VoIP) communication often uses variable bit rate (VBR) codecs to achieve good audio quality while minimizing bandwidth costs. Prior work has shown that encrypted VBR-based VoIP streams are vulnerable to re-identification attacks in which an attacker can infer attributes (e.g., the language being spoken, the identities of the speakers, and key phrases) about the underlying audio by analyzing the distribution of packet sizes. Existing defenses require the participation of both the sender and receiver to secure their VoIP communications. This paper presents Whisper, the first unilateral defense against re-identification attacks on encrypted VoIP streams. Whisper works by modifying the audio signal before it is encoded by the VBR codec, adding inaudible audio that either falls outside the fixed range of human hearing or is within the human audible range but is nearly imperceptible due to its low amplitude. By carefully inserting such noise, Whisper modifies the audio stream's distribution of packet sizes, significantly decreasing the accuracy of re-identification attacks. Its use is imperceptible by the (human) receiver. Whisper can be instrumented as an audio driver and requires no changes to existing (potentially closed-source) VoIP software. Since it is a unilateral defense, it can be applied at will by a user to enhance the privacy of its voice communications. We demonstrate that Whisper significantly reduces the accuracy of re-identification attacks and incurs only a small degradation in audio quality. 

Voice synthesis uses a voice model to synthesize arbitrary phrases. Advances in voice synthesis have made it possible to create an accurate voice model of a targeted individual, which can then in turn be used to generate spoofed audio in his or her voice. Generating an accurate voice model of target’s voice requires the availability of a corpus of the target’s speech. This paper makes the observation that the increasing popularity of voice interfaces that use cloud-backed speech recognition (e.g., Siri, Google Assistant, Amazon Alexa) increases the public’s vulnerability to voice synthesis attacks. That is, our growing dependence on voice interfaces fosters the collection of our voices. As our main contribution, we show that voice recognition and voice accumulation (that is, the accumulation of users’ voices) are separable. This paper introduces techniques for locally sanitizing voice inputs before they are transmitted to the cloud for processing. In essence, such methods employ audio processing techniques to remove distinctive voice characteristics, leaving only the information that is necessary for the cloud-based services to perform speech recognition. Our preliminary experiments show that our defenses prevent state-of-the-art voice synthesis techniques from constructing convincing forgeries of a user’s speech, while still permitting accurate voice recognition.