Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy when retrieving data from public clouds. Our system employs TFHE’s expertise in non-polynomial evaluations, crucial for comparison operations. LocPIR showcases minimal client-server interaction, reduced memory overhead, and efficient throughput. Performance tests confirm its computational speed, making it a viable solution for practical scenarios, demonstrated via application to a COVID-19 alert model. Thus, LocPIR effectively addresses privacy concerns in location-based services, enabling secure data sharing from the public cloud.

Recent speech-based services such as voice assistants and cloud computing services have brought security concerns, since those services constantly send user's speech data to the server. Speech data contain user's sensitive biometric data and spoken words and can be misused if it is leaked into wrong hands. In this context, Signal Processing in Encrypted Domain (SPED) can be a solution by mixing up Homomorphic Encryption (HE) with signal processing. Using HE enables computing on user's encrypted data without decrypting it, thus providing security and privacy. In this paper, we present a simple, but fast homomorphic Quantized Fourier Transform (QFT) with efficient packing of speech signals. Our work is based on the Fully Homomor-phic Encryption (FHE) scheme TFHE, which was proposed by Chillotti et al. We then present a thorough noise analysis of our QFT that helps to keep a reasonable noise level. Also, considering the TFHE's bootstrapping manner, we statistically analyze the boundary of the QFT coefficients, and present a simple criterion for scaling up the coefficients. Our criteria help keep the message precision as high as possible during the TFHE bootstrapping. We use our criteria to evaluate the magnitude of QFT with low latency, but with reasonable precision. Finally, we provide a proof-of-concept implementation of our QFT. With a ring dimension of 1024 and TFHE parameters that achieve 106 bits of security, we show that the QFT can be evaluated in 35 milliseconds for a single ciphertext of length 1024. This result is 74.6 times faster than in the previous work. We also built a homomorphic end-to-end speech processing framework that processes and classifies gender (resp. vowel) of encrypted speech data from the VoxCeleb (resp. PCVC) dataset. Our implementation classifies the gender (resp. vowel) with more than 83% (resp. 79%) accuracy with a minimum of 0.05 (resp. 0.56) seconds with multithreading.

Smartphone sensors potentially threaten the privacy of individuals, placing society at risk. Previous studies have demonstrated that smartphone sensors are susceptible to privacy intrusion. Inspired by this finding, we designed a mechanism of invasion that targets the location privacy of subway passengers. Specifically, we recovered the travel trajectories of subway passengers using sensor data and matched them with railway data collected from OpenStreetMap. This study primarily exploits an accelerometer and gyroscope, which are suitable for subway tracking because they operate appropriately in underground and indoor conditions. Although these sensors are easily influenced by passenger activity, we devised a method for recovering clean trajectories of subway passengers by utilizing gravitational acceleration and event detection methods. Subsequently, we conducted several experiments to prove the threat and feasibility of our proposals, even in the presence of human-generated noise (e.g., texting, watching videos, playing games, device rotation, and changing positions) influencing the sensor data. Specifically, we applied dynamic time warping (DTW) to obtain the costs between the reference data and reconstructed trace. Finally, a cost combination mechanism aggregated the DTW costs and predicted the best matches.

This paper proposes a new clock offset estimation that mitigates unwanted link asymmetry for precise clock synchronization. The main contribution is to address the primary and traditional design issue of the IEEE 1588 standard precision time protocol (PTP), which estimates clock offset under the assumption that the delays of exchanged packets are symmetric. To mitigate the issue, we focus on the fact that PTP measures asymmetry variation through the derivatives of its timestamps with respect to the time step. By exploiting the measurement of the variation, the proposed approach defines the asymmetry in the form of a linear differential equation (LDE) and leverages the LDE to define and exclude asymmetry-induced errors. Additionally, we clearly derive the state transition of the asymmetry. Subsequently, we derive a novel state-space model from our approach. The model describes PTP clock offset estimation perfectly, allowing optimal clock offset estimation. We verify the theoretical validity of the proposed method with real data. Our approach improves PTP accuracy by more than thousand times and achieves an accuracy at the level of tens to hundreds of nanoseconds on an asymmetric communication link. Our approach realizes an accuracy comparable to that of PTPv2, without the cost of specialized hardware.

Using electrical network frequency (ENF) for video forensics has been intensely studied in recent years. The ENF signal found in videos has twice the electrical frequency (100 Hz or 120 Hz), whereas frame rates of common videos are relatively low (around 30 Hz). To extract ENF signal from video, state-of-the-art works exploit the rolling shutter effect. However, this method has a constraint that the region affected by the flickering light has to be large enough to contain all the information which light leaves at the pixels. As these regions are only part of the scene in many cases, it is hard to take advantage of the rolling shutter effect. In this paper, we propose a novel method to extract ENF signals by topological approach without utilizing the rolling shutter effect. Based on the fact that the topological representation of the possible outcomes is in the form of a closed-loop, we obtain the phase angles of each frame using manifold learning. We convert the phase angles into the frequency values based on the prior knowledge about the nominal frequency of ENF and the frame rate of the video. We tested two different manifold learning algorithms (i.e., UMAP and t-SNE) and compared the result with the state-of-the-art works, and t-SNE shows the best performance achieving root-mean-square error (RMSE) of 0.00036 Hz.

Electric Network Frequency (ENF) analysis has been an intriguing tool for multimedia forensics as former studies have paved the way for estimating ENF signals from digital audio, video, or even image files. However, for ENF signals to be widely used in extensive applications, supplementary research is needed so that ENF signals can be stably extracted without restrictions. In this letter, we propose a new phase-based approach for extracting ENF signals from CMOS sensor recordings. It uses phase differences between row signals from two consecutive frames, such that problems due to missing sample points during the idle periods are circumvented. The proposed method has substantial advantages in that it is applicable without a predefined read-out time and when the length of given videos is too short. Extensive experiments conducted with numerous devices demonstrate that the proposed method can take precedence over state-of-the-art methods because it robustly produces accurate ENF estimates in terms of alias frequency on the frame-level. The coding framework used for this letter is available at:https://github.com/hyekyunghan/Phase-based-ENF-extraction-method.

The “2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge” (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy - this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019 dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, surpassing the top ensemble system from the competition that achieved an EER of 0.39%.

Voice assistants are quickly being upgraded to support advanced, security-critical commands such as unlocking devices, checking emails, and making payments. In this paper, we explore the feasibility of using users' text-converted voice command utterances as classification features to help identify users' genuine commands, and detect suspicious commands. To maintain high detection accuracy, our approach starts with a globally trained attack detection model (immediately available for new users), and gradually switches to a user-specific model tailored to the utterance patterns of a target user. To evaluate accuracy, we used a real-world voice assistant dataset consisting of about 34.6 million voice commands collected from 2.6 million users. Our evaluation results show that this approach is capable of achieving about 3.4% equal error rate (EER), detecting 95.7% of attacks when an optimal threshold value is used. As for those who frequently use security-critical (attack-like) commands, we still achieve EER below 5%.

The electric network frequency (ENF) has a statistical uniqueness according to time and location. The ENF signal is always slightly fluctuating for the load balance of the power grid around the fundamental frequency. The ENF signals can be obtained from the power line using a frequency disturbance recorder (FDR). The ENF signal can also be extracted from video files or audio files because the ENF signal is also saved due to the influence of the electromagnetic field when video files or audio files are recorded. In this paper, we propose a method to find power grid from ENF signals collected from various time and area. We analyzed ENF signals from the distribution level of the power system and online uploaded video files. Moreover, a hybrid feature extraction approach, which employs several features, is proposed to infer the location of the signal belongs regardless of the time that the signal was collected. Employing our suggested feature extraction methods, the signal which extracted from the power line can be classified 95.21% and 99.07% correctly when ENF signals have 480 and 1920 data points, respectively. In the case of ENF signals extracted from multimedia, the accuracy varies greatly according to the recorded environment such as network status and microphone quality. When constructing a feature vector from 120 data points of ENF signals, we could identify the power grid had an average of 94.17% accuracy from multimedia.

Homomorphic encryption (HE) is considered as one of the most powerful solutions to securely protect clients’ data from malicious users and even severs in the cloud computing. However, though it is known that HE can protect the data in theory, it has not been well utilized because many operations of HE are too slow, especially multiplication. In addition, existing data mining research studies using encrypted data focus on implementing only specific algorithms without addressing the fundamental problem of HE. In this paper, we propose a fundamental design and implementation of data mining algorithm through logical gates. In order to do this, we design various logic of atomic operations in encrypted domain and finally apply these logic to well-known data mining algorithms. We also analyze the execution time of atomic and advanced algorithms.

Electrical network frequency (ENF) signals have common patterns that can be used as signatures for identifying recorded time and location of videos and sound. To enable cost-efficient, reliable and scalable location inference, we created a reference map of ENF signals representing hundreds of locations world wide -- extracting real-world ENF signals from online multimedia streaming services (e.g., YouTube and Explore). Based on this reference map of ENF signals, we propose a novel side-channel attack that can identify the physical location of where a target video or sound was recorded or streamed from. Our attack does not require any expensive ENF signal receiver nor any software to be installed on a victim»s device -- all we need is the recorded video or sound files to perform the attack and they are collected from world wide web. The evaluation results show that our attack can infer the intra-grid location of the recorded audio files with an accuracy of $76$% when those files are $5$ minutes or longer. We also showed that our proposed attack works well even when video and audio data are processed within a certain distortion range with audio codecs used in real VoIP applications.

The frequency of power distribution networks in a power grid is called electrical network frequency (ENF). Because it provides the spatio-temporal changes of the power grid in a particular location, ENF is used in many application domains including the prediction of grid instability and blackouts, detection of system breakup, and even digital forensics. In order to build high performing applications and systems, it is necessary to capture a large-scale nationwide or worldwide ENF map. Consequently, many studies have been conducted on the distribution of specialized physical devices that capture the ENF signals. However, this approach is not practical because it requires significant effort from design to setup, moreover, it has a limitation in its efficiency to monitor and stably retain the collection equipment distributed throughout the world. Furthermore, this approach requires a significant budget.

There have been many efforts to strengthen security of Instant Messaging (IM) system. One of the typical technologies is the conventional message encryption using a secret or private key. However, the key is fundamentally vulnerable to a brute-force attack, causing to acquire the original message. In this respect, a countermeasure was suggested as the way to generating plausible-looking but fake plaintexts, which is called Honey Encryption (HE). In this paper, we present a HE-based statistical scheme and design a Honey Chatting application, which is robust to eavesdropping. Besides, we verify the effectiveness of the Honey Chatting by comparing the entropy of decrypted messages through experiments.

Honey encryption (HE) is a new technique to overcome the weakness of conventional password-based encryption (PBE). However, conventional honey encryption still has the limitation that it works only for binary bit streams or integer sequences because it uses a fixed distribution-transforming encoder (DTE). In this paper, we propose a variant of honey encryption called visual honey encryption which employs an adaptive DTE in a Bayesian framework so that the proposed approach can be applied to more complex domains including images and videos. We applied this method to create a new steganography scheme which significantly improves the security level of traditional steganography.

In this paper, we present a personal journey advisor application for helping people to navigate the city using the available bike-sharing system. For a given origin and destination, the application suggests the best pair of stations to be used to take and return a city-bike, in order to minimize the overall walking and biking travel time as well as maximizing the probability to find available bikes at the first station and returning slots at the second one. To solve the journey advisor optimization problem, we modeled real mobile bikers' behavior in terms of travel time, and used the predicted availability at every bike station to choose the pair of stations which maximizes a measure of optimality. To develop the application, we built a spatio-temporal prediction system able to estimate the number of available bikes for each station in short and long term, outperforming already developed solutions. The prediction system is based on an underlying spatial interaction network among the bike stations, and takes into account the temporal patterns included in the data. The City ride application was tested with real data from the Dublin bike-sharing system.

The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid the problem of failing of misled mean shift vectors. As a result, we can obtain a more robust clustering result compared to the conventional mean shift algorithm. We also propose an alternative way to assign weights for large size datasets and noisy datasets.

Selection schemes for forming data validation regions for target tracking are discussed in this paper. We develop a novel algorithm, less sensitive to gate size than conventional approaches. This new gate selection method combines a conventional threshold based algorithm with a geometric metric measure based on theVoronoi diagram. An adaptive search based on the Voronoi measure is then used to select valid data points for target tracking.

A mobile ad hoc network (MANET) is a decentralized network of mobile nodes. Due to the broadcast nature of radio transmissions, communication in MANETs is more susceptible to malicious traffic analysis. An interesting problem is how to thwart malicious traffic analysis. Most anonymous communication protocols are based on the pseudonyms of mobile nodes. However, conventional pseudonym schemes have some limitations such as collisions of pseudonyms and high computational complexity due to the use of cryptographic hash functions. Collisions of identities are not desirable since they are the main causes for reduced effective bandwidth, increased energy consumption and non-deterministic data delivery. In this paper, we propose a new collision-free pseudonym scheme to enable anonymous communication. In our approach, each node generates pseudonyms by using a permutation matrix without collisions. The challenging issue is how to store the overall permutation matrix. It is practically hard to assume that mobile nodes maintain the permutation matrix due to the limitation of resources. Therefore we design the online computation of each node's own pseudonym without loading the overall matrix.