arXiv subjects

Muhammad Haris Mughees

Publications and source records attributed to Muhammad Haris Mughees.

4 recordsLinked to original sources

arXiv2024

Wally: Batched Private Nearest Neighbor Search at Scale

We present Wally, a batched private nearest-neighbor search protocol that uses differential privacy to break the linear computation barrier of fully-oblivious schemes. In Tiptoe, the server must process the entire database per query to hide the access pattern, resulting in low throughput (909 QPS) and high communication (17.4 MB) on a 3.2M-entry database. Sublinear alternatives like Pacmann require 614 MB of client storage and an offline streaming phase. Wally's key insight is that fully-oblivious schemes are prohibitively expensive at scale, but the same scale also provides an opportunity. Large-scale systems naturally have many concurrent clients. Wally batches queries from non-coordinating clients, each independently adding fake queries to hide which clusters it accesses. The fake query counts follow a negative binomial distribution, which is non-negative and infinitely divisible, allowing independent sampling without coordination. Clients send queries at random times through an existing anonymization service, avoiding a centralized shuffler. The server sees only an anonymized, noisy stream of cluster accesses that is provably (epsilon, delta)-differentially private, computing over only the relevant clusters. The client encrypts its query under SHE so the server returns only encrypted similarity scores. On a 3.2M-entry database with 500K-query batches, Wally achieves 7-29x higher throughput and 6.7-31x lower communication than Tiptoe, and 15,000x lower client storage than Pacmann, with strong (epsilon=0.1, delta=2^{-26})-DP and comparable accuracy. We also propose optimizations to SHE and keyword PIR yielding 2-3x improvements in PIR and 20-25% in BFV operations, and release an open-source BFV library in Swift.

cs.CR↗

arXiv2021

PrivateFetch: Scalable Catalog Delivery in Privacy-Preserving Advertising

In order to preserve the possibility of an Internet that is free at the point of use, attention is turning to new solutions that would allow targeted advertisement delivery based on behavioral information such as user preferences, without compromising user privacy. Recently, explorations in devising such systems either take approaches that rely on semantic guarantees like $k$-anonymity -- which can be easily subverted when combining with alternative information, and do not take into account the possibility that even knowledge of such clusters is privacy-invasive in themselves. Other approaches provide full privacy by moving all data and processing logic to clients -- but which is prohibitively expensive for both clients and servers. In this work, we devise a new framework called PrivateFetch for building practical ad-delivery pipelines that rely on cryptographic hardness and best-case privacy, rather than syntactic privacy guarantees or reliance on real-world anonymization tools. PrivateFetch utilizes local computation of preferences followed by high-performance single-server private information retrieval (PIR) to ensure that clients can pre-fetch ad content from servers, without revealing any of their inherent characteristics to the content provider. When considering an database of $>1,000,000$ ads, we show that we can deliver $30$ ads to a client in 40 seconds, with total communication costs of 192KB. We also demonstrate the feasibility of PrivateFetch by showing that the monetary cost of running it is less than 1% of average ad revenue. As such, our system is capable of pre-fetching ads for clients based on behavioral and contextual user information, before displaying them during a typical browsing session. In addition, while we test PrivateFetch as a private ad-delivery, the generality of our approach means that it could also be used for other content types.

cs.CR↗

arXiv2016

Smartphone Fingerprinting Via Motion Sensors: Analyzing Feasibility at Large-Scale and Studying Real Usage Patterns

Advertisers are increasingly turning to fingerprinting techniques to track users across the web. As web browsing activity shifts to mobile platforms, traditional browser fingerprinting techniques become less effective; however, device fingerprinting using built-in sensors offers a new avenue for attack. We study the feasibility of using motion sensors to perform device fingerprinting at scale, and explore countermeasures that can be used to protect privacy. We perform a large-scale user study to demonstrate that motion sensor fingerprinting is effective with even 500 users. We also develop a model to estimate prediction accuracy for larger user populations; our model provides a conservative estimate of at least 12% classification accuracy with 100000 users. We then investigate the use of motion sensors on the web and find, distressingly, that many sites send motion sensor data to servers for storage and analysis, paving the way to potential fingerprinting. Finally, we consider the problem of developing fingerprinting countermeasures; we evaluate a previously proposed obfuscation technique and a newly developed quantization technique via a user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.

cs.CY↗

arXiv2016

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets -- small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84% at the time-interval level, and 0.05% at the IP-level with 100% true positive rates at both.

cs.CR↗