Data stream collection is critical to analyze service conditions and detect anomalies in time, especially in Internet of Things. However, it may undermine the individual privacy. Local differential privacy (LDP) has recently become a popular privacy-preserving technique protecting users’ privacy. However, most of them are still limited to the assumption of one-item collection, resulting in poor utility when extended to the multi-item collection from a very large domain. This article proposes a private streaming data collection framework, private sketch-based framework (PSF), which takes advantage of sketches. Combining the proposed background information and a decode-first collection-side workflow, the framework improves the utility by reducing the errors introduced by the sketching algorithm and the privacy budget utilization when collecting multiple items. We analytically prove the superior accuracy and privacy characteristics of PSF. In order to support specific computing tasks, we build two private protocols based on PSF, PrivSketch and PrivSketch+, aiming at frequency estimation and mean estimation, respectively. We demonstrate the utility of PrivSketch and PrivSketch+ theoretically, and also evaluate them experimentally. Our evaluation, with several diverse synthetic and real data sets, demonstrates that PrivSketch is 1–3 orders of magnitude better than the competitors in terms of utility in both frequency estimation and frequent item estimation, while being up to ~100x faster. PrivSketch+ performs ~4 orders of magnitude better than advanced solutions, such as piecewise mechanism (PM) and hybrid mechanism (HM), under a limited privacy budget.

In recent years, more and more wireless networks support both 2.4GHz and 5GHz bands. However, in large-scale dual-band wireless networks, lack of understanding on the behavior and performance makes the network diagnosis and optimization extremely challenging. In this paper, we conduct a comprehensive measurement to characterize the behavior and performance in a large-scale dual-band wireless network (TD WLAN). We make several meaningful observations. (1) Although the 5GHz band outperforms the 2.4GHz band, 60% of devices tend to be associated with the 2.4GHz band. The device association behavior has a large impact on the performance. (2) Rogue and non-WiFi devices are prevalent, wherein hidden terminal interference increases the average loss rate by 8%, carrier sense interference increases the average WiFi latency by 45%, and RF interference further aggravates both packet loss and channel contention. (3) The dynamic channel assignment strategy is not always effective. On this basis, we propose a novel and easy-to-implement strategy to improve the wireless performance by intelligent band navigation and heuristic channel optimization. The actual deployment in TD WLAN shows the packet loss reduces by 40% on average and the WiFi latency for more than 60% of devices is below 5ms.

1Network measurement is indispensable to network operations. Two most promising measurement solutions are In-band Network Telemetry (INT) solutions and sketching solutions. INT solutions provide fine-grained per-switch per-packet information at the cost of high network overhead. Sketching solutions have low network overhead but fail to achieve both simplicity and accuracy for per-flow measurement. To keep their advantages, and at the same time, overcome their shortcomings, we first design SketchINT to combine INT and sketches, aiming to obtain all per-flow per-switch information with low network overhead. Second, for deployment flexibility and measurement accuracy, we design a new sketch for SketchINT, namely TowerSketch, which achieves both simplicity and accuracy. The key idea of TowerSketch is to use different-sized counters for different arrays under the property that the number of bits used for different arrays stays the same. TowerSketch can automatically record larger flows in larger counters and smaller flows in smaller counters. We have fully implemented our SketchINT prototype on a testbed consisting of 10 switches. We also implement our TowerSketch on P4, single-core CPU, multi-core CPU, and FPGA platforms to verify its deployment flexibility. Extensive experimental results verify that 1) TowerSketch achieves better accuracy than prior art on various tasks, outperforming the state-of-the-art ElasticSketch up to 13.9 times in terms of error; 2) Compared to INT, SketchINT reduces the number of packets in the collection process by 3 4 orders of magnitude with an error smaller than 5%.

Anycast content delivery networks rely on the underlying routing to schedule clients to their nearby service nodes, which however is not natively aware of server load or path latency. Requests burst from some regions may cause overload and hurt user experience. This scenario demands quickly adjusting clients to other nearby servers with available capacity. However, state-of-the-art solutions do not work well. On one hand, native routing-based scheduling is not flexible and precise enough, which may cause cascading damage and interrupt ongoing sessions. On the other hand, centralized algorithm is vulnerable and not responsive due to high complexity. We propose a practical distributed hybrid load management model to solve load burst problem. First, the hybrid mechanism leverages flexible DNS-based redirection, which can schedule at per-request granularity without interrupting ongoing sessions. Second, the distributed model is responsive by reducing computation overhead and theoretically guarantees to converge to the optimal solution. Based on the model, we further propose an cooperative and two heuristic distributed algorithms. At last, using a measurement dataset, we demonstrate their effectiveness and scalability, and illustrate how to adapt them to different scenarios.

Dynamic Host Configuration Protocol (DHCP) is widely used to dynamically assign IP addresses to users. However, due to little knowledge on the behavior and performance of DHCP, it is challenging to configure lease time and divide IP addresses for address pools properly in large-scale wireless networks. In this paper, we conduct the largest known measurement on the behavior and performance of DHCP in the wireless network of T University (TWLAN). We find the performance of DHCP is far from satisfactory: (1) The non-authenticated devices lead to a waste of 25% of addresses at the rush hour. (2) Address pool utilization varies greatly under the current address division strategy. (3) A device does not generate traffic for 67% of the lease time on average. Meanwhile, we observe devices of different locations and operating systems show diverse online patterns. A unified lease time setting could result in an inefficient usage of addresses. To address the problems, taking account of authentication information and online patterns, we propose a new leasing strategy. The results show it outperforms three state-of-the-art baselines and reduces the number of assigned addresses by 24% and the average total lease time by 17% without significantly increasing the DHCP server load. Besides, we further propose an adaptive address division strategy to balance the address utilization of pools, which can be deployed in parallel with the new leasing strategy and reduce the risk of address exhaustion.

Anycast has been increasingly deployed for content delivery networks to map clients to their nearby replicas, which relies on the underlying routing. However, the simplicity of operation comes at cost of less precise client-mapping control. Although many works have measured anycast DNS, anycast CDNs, with different service goals and engineering, are still not fully understood. In this paper, we design novel methods and combine large-scale traceroute and HTTP measurement to evaluate the overall client-proximity and inefficient routing of the largest anycast CDN, Cloudflare. We find that 90% paths traverse only 2-4 ASes, which highlights its direct networks providers. By further identifying and characterizing direct providers at finer granularity of facilities, we quantitatively shows that Cloudflare unevenly uses few large transit providers to delivery the majority of contents. Inspired by the observations, we propose an anycast routing pathology and diagnosis methodology. Investigation reveals that few huge providers have outsized impact in that they are not only related to many inter-domain inflations, but also have path inflation inside their own networks, thus deserving priority focus when troubleshooting.

Dynamic Host Configuration Protocol (DHCP) is widely used to dynamically assign IP addresses. However, due to little knowledge on the behavior and performance of DHCP, it is challenging to configure a proper lease time in complicated wireless network. In this paper, we conduct the largest known measurement on the behavior and performance of DHCP based on the wireless network of T University (TWLAN). TWLAN has more than 59,000 users, 10,000 wireless access points and 130,000 IP addresses. We find the performance of DHCP is far from satisfactory: (1) The non-authenticated devices lead to a waste of 25% of IP addresses at the rush hour. (2) A device does not generate traffic for 67 % of the lease time on average. Meanwhile, we find devices of different locations and operating systems show diverse online patterns. A unified lease time setting could result in an inefficient utilization of addresses. To address the problems, taking account of authentication information and device online patterns, we propose a new leasing strategy. The results show it reduces the number of assigned addresses by 24 % and reduces the time during which an IP address is occupied by 17 % without sianificantly Increasing the DHCP server load.

The worldwide distribution of IPv4 addresses is highly non-uniform. The factors that contribute to IPv4 address resources owned by a country remain unknown. In this paper, we systematically study the relationship between the number of allocated IPv4 addresses and 30 factors extracted from political, economic, ecological, social, and cultural dimensions. We observe that GDP has the largest influence on the number of IPv4 addresses while the influence of political factors is very weak. To further quantify the complex relationship among them, we fit data into a Decision Tree model. Based on the pruned model, we can extract precise rules that contribute to IPv4 address resources owned by a country. To the best of our knowledge, this is the first work to evaluate IPv4 address ownership by quantitative factors. Our work is helpful to better understand the network development of a country.